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Image Classification Based on Video Segments

Abstract

In the dissertation a new method for improving the quality of classifications of images in video streams has been proposed and analyzed. In multiple fields concerning such a classification, the proposed algorithms focus on the analysis of single frames. This class of algorithms has been named OFA (One Frame Analyzed).In the dissertation, small segments of the video are considered and each image is analyzed in the context of its closest neighborhood, which is defined by a shifting time window. The class of algorithms representing such an approach has been named FSA (Frame Sequence Analyzed).Experiments on a number of video streams of different types have confirmed that the FSA method improves the classification results by reducing the level of error on average by 20%. Two variants of FSA algorithms have been analyzed: iFSA - which considers only OFA decisions, and fFSA - which considers OFA decisions as well as the similarity between the analyzed frames. Furthermore, the variants differ in terms of their computational complexity. The analysis of the proposed FSA algorithms included different configurations of decision functions, multiple similarity measures, as well as method parameters such as: the window size, the significance weight distribution parameter or the decision acceptance threshold. The FSA algorithms have been evaluated in terms of those attributes, which has proven their applicability in terms of the type and intensity of distortions in the video stream. Furthermore, the performed tests have confirmed the effectiveness and versatility of the FSA method.

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Category:
Thesis, nostrification
Type:
praca doktorska pracowników zatrudnionych w PG oraz studentów studium doktoranckiego
Language:
English
Publication year:
2018
Bibliography: test
  1. S. Abdallah, M. Sandler, C. Rhodes, and M. Casey. Using duration models to reduce fragmentation in audio segmentation. Machine Learning, 65(2-3):485-515, nov 2006. open in new tab
  2. S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G.-Z. Yang, and N. Navab. Endoscopic video manifolds. Medical Image Computing and Computer-Assisted Intervention, 13(Pt 2):437-445, 2010. open in new tab
  3. Australian Bureau of Statistics. An Introductory Course on Time Series Analysis -Electronic Delivery. 2005. open in new tab
  4. M. Badurowicz, T. Cieplak, and J. Montusiewicz. The Cloud Computing Stream Analysis System for Road Artefacts Detection. In Computer Networks, pages 360-369. 2016. open in new tab
  5. M. Badurowicz, T. Cieplak, and J. T. Montusiewicz. On-the-fly community-driven mobile accelerometer data analysis system for road quality assessment. Applied Computer Science, 12(4):18-27, 2016. open in new tab
  6. S. Bai, J. Z. Kolter, and V. Koltun. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv:1803.01271, mar 2018.
  7. H. G. Barrow, J. M. Tenenbaum, R. C. Bolles, and H. C. Wolf. Parametric correspondence and chamfer matching: two new techniques for image matching. Proceedings of the 5th international joint conference on Artificial intelligence -Volume 2, pages 659-663, 1977.
  8. S. Basu and M. Meckesheimer. Automatic outlier detection for time series: an application to sensor data. Knowledge and Information Systems, 11(2):137-154, aug 2006. open in new tab
  9. A. Bellet, A. Habrard, and M. Sebban. A Survey on Metric Learning for Feature Vectors and Structured Data. Technical report, jun 2013. open in new tab
  10. R. Bellman and R. Ernest. Dynamic programming. Dover Publications, 2003. open in new tab
  11. A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer. MOA: Massive Online Analysis. Journal of Machine Learning Research, 11(May):1601-1604, 2010. open in new tab
  12. A. Blokus and H. Krawczyk. Systematic Approach to Binary Classification of Images in Video Streams using Shifting Time-Windows. Signal, Image and Video Processing, ((Accepted for publication)), 2018. open in new tab
  13. A. Blokus and H. Krawczyk. Improving Traffic Light Recognition Methods using Shifting Time-Windows. In 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), pages 1-5, Maribor, jun 2018. IEEE. open in new tab
  14. A. Blokus and H. Krawczyk. Improving methods for detecting people in video recordings using shifting time-windows. In X. Jiang and J.-N. Hwang, editors, Tenth International Conference on Digital Image Processing (ICDIP 2018), volume 10806, page 121, Shanghai, aug 2018. SPIE. open in new tab
  15. A. Blokus and H. Krawczyk. Impact of shifting time-window post-processing on the quality of face detection algorithms. In 2018 11th International Conference on Human System Interaction (HSI), pages 77-83. IEEE, jul 2018. open in new tab
  16. A. Blokus, A. Brzeski, J. Cychnerski, T. Dziubich, and M. Jȩdrzejewski. Real-Time Gastrointestinal Tract Video Analysis on a Cluster Supercomputer. In W. Zamojski, J. Mazurkiewicz, J. Sugier, T. Walkowiak, and J. Kacprzyk, editors, Complex Systems and Dependability, volume 170 AISC, pages 55-68. Springer, 2012. open in new tab
  17. A. Blokus, A. Brzeski, J. Cychnerski, and M. Jȩdrzejewski. Endoscopic Video Classification with the Consideration of Temporal Patterns. In Proceedings of the 5th International Interdisciplinary Technical Conference of Young Scientists InterTech 2012, pages 237-241, Poznań, 2012. Wydawnictwo Politechniki Gdańskiej. open in new tab
  18. A. Blokus, A. Brzeski, and J. Cychnerski. Issues of classification function continuity in endoscopic video clas- sification. In Zeszyty Naukowe Wydziału Elektroniki, Telekomunikacji i Informatyki Politechniki Gdańskiej, Publikacja konferencyjna ICT Young, 2013. open in new tab
  19. A. Blokus, J. Cychnerski, and A. Brzeski. Accelerating video frames classification with metric based scene segmentation. International Journal of Innovative Research in Computer and Communication Engineering, 2(8):5311-5315, 2014. open in new tab
  20. Bo Han and Weiguo Wu. Video scene segmentation using a novel boundary evaluation criterion and dynamic programming. In 2011 IEEE International Conference on Multimedia and Expo, pages 1-6. IEEE, jul 2011.
  21. E. Z. Borzeshi, O. P. Concha, M. Piccardi, O. Perez Concha, and R. Y. D. Xu. Joint Action Segmentation and Classification by an Extended Hidden Markov Model. IEEE Signal Processing Letters, 20(12):1207- 1210, dec 2013. open in new tab
  22. S. Bourennane and C. Fossati. Comparison of shape descriptors for hand posture recognition in video. Signal, Image and Video Processing, 6(1):147-157, mar 2012. open in new tab
  23. G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.
  24. J. Brankov, M. Wernick, M. King, Y. Yang, and M. Narayanan. Spatially Adaptive Temporal Smoothing for Reconstruction of Dynamic Image Sequences. IEEE Transactions on Nuclear Science, 53(5):2769-2777, oct 2006. open in new tab
  25. J. J. G. Brankov, M. M. N. Wernick, M. M. V. Narayanan, and Y. Yang. Spatially-adaptive temporal smoothing for reconstruction of dynamic and gated image sequences. In 2000 IEEE Nuclear Science Sym- posium. Conference Record (Cat. No.00CH37149), volume 2, pages 15/146-15/150. IEEE, 2000. open in new tab
  26. A. Brzeski, A. Blokus, and J. Cychnerski. An Overview of Image Analysis Techniques in Endoscopic Bleeding Detection. International Journal of Innovative Research in Computer and Communication Engineering, 1 (5), 2013.
  27. M. Burgin. Continuity in Discrete Sets. arXiv:1002.0036, jan 2010. open in new tab
  28. W. Cai, Y. Song, and D. D. Feng. Regression and classification based distance metric learning for medical image retrieval. In 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pages 1775- 1778. IEEE, may 2012. open in new tab
  29. Y. Cao, D. Liu, W. Tavanapong, J. Wong, J. Oh, and P. C. De Groen. Computer-aided detection of diagnostic and therapeutic operations in colonoscopy videos. IEEE Transactions on Biomedical Engineering, 54(7):1268-1279, 2007.
  30. R. D. Charette and F. Nashashibi. Real Time Visual Traffic Lights Recognition with Image Processing. Advanced Robotics, (33):358-363, 2009. open in new tab
  31. Y. Chen and J. Lee. A review of machine-vision-based analysis of wireless capsule endoscopy video. Diag- nostic and therapeutic endoscopy, 2012:418037, jan 2012. open in new tab
  32. D. Cox and E. J. Snell. Analysis of Binary Data, Second Edition. CRC Press, 1989. open in new tab
  33. J. Cychnerski, A. Brzeski, A. Blokus, T. Dziubich, and M. Jȩdrzejewski. Konstrukcja bazy danych dla systemu wspomagania diagnostyki chorób przewodu pokarmowego. In Studia Informatica, volume 33, 2012.
  34. J. Cychnerski, A. Brzeski, and A. Blokus. Method of training the endoscopic video analysis algorithms to maximize both accuracy and stability. In ICT Young 2013, number 10, 2013.
  35. K. Czuszynski, J. Ruminski, and A. Kwasniewska. Gesture Recognition With the Linear Optical Sensor and Recurrent Neural Networks. IEEE Sensors Journal, 18(13):5429-5438, jul 2018. open in new tab
  36. J. Dai, Y. Li, K. He, and J. Sun. R-FCN: Object Detection via Region-based Fully Convolutional Networks. arXiv preprint, arXiv:1605.06409, may 2016.
  37. R. de Charette and F. Nashashibi. Traffic light recognition using image processing compared to learning processes. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 333-338, oct 2009. open in new tab
  38. O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce. Automatic annotation of human actions in video. In 2009 IEEE 12th International Conference on Computer Vision, pages 1491-1498. IEEE, sep 2009. open in new tab
  39. C. Dwork, R. Kumar, M. Naor, and D. Sivakumar. Rank aggregation methods for the Web. In Proceedings of the tenth international conference on World Wide Web -WWW '01, pages 613-622, New York, New York, USA, 2001. ACM Press. open in new tab
  40. M. Ebdelli, O. Le Meur, and C. Guillemot. Video Inpainting With Short-Term Windows: Application to Object Removal and Error Concealment. IEEE Transactions on Image Processing, 24(10):3034-3047, oct 2015. open in new tab
  41. S. Erturk. Image sequence stabilisation: motion vector integration (MVI) versus frame position smoothing (FPS). In ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat. No.01EX480), pages 266-271. Univ. Zagreb, 2001. open in new tab
  42. A. Fabijanska and J. Goclawski. The Segmentation of 3D Images Using the Random Walking Technique on a Randomly Created Image Adjacency Graph. IEEE Transactions on Image Processing, 24(2):524-537, feb 2015. open in new tab
  43. C. Farabet, C. Couprie, L. Najman, and Y. LeCun. Learning Hierarchical Features for Scene Labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1915-1929, aug 2013. open in new tab
  44. J. Felipe, A. Machado Traina, and C. Traina. Global Warp Metric Distance: Boosting Content-based Image Retrieval through Histograms. In Seventh IEEE International Symposium on Multimedia (ISM'05), pages 295-302. IEEE, 2005. open in new tab
  45. G. Fettweis and H. Meyr. Parallel Viterbi algorithm implementation: breaking the ACS-bottleneck. IEEE Transactions on Communications, 37(8):785-790, 1989. open in new tab
  46. G. Fettweis and H. Meyr. Feedforward architectures for parallel viterbi decoding. Journal of VLSI signal processing systems for signal, image and video technology, 3(1-2):105-119, jun 1991. open in new tab
  47. D. Figueira, M. Taiana, J. C. Nascimento, and A. Bernardino. A Window-Based Classifier for Automatic Video-Based Reidentification. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(12): 1736-1747, dec 2016. open in new tab
  48. G. Forney. The viterbi algorithm. Proceedings of the IEEE, 61(3):268-278, 1973. open in new tab
  49. B. Froba and C. Kublbeck. Face Tracking by Means of Continuous Detection. In 2004 Conference on Computer Vision and Pattern Recognition Workshop, page 65. IEEE, 2004. open in new tab
  50. G. Gallo and A. Torrisi. Boosted Wireless Capsule Endoscopy Frames Classification. In PATTERNS 2011, The Third International Conferences on Pervasive Patterns and Applications, pages 25-30, 2011. open in new tab
  51. G. Gallo, E. Granata, and G. Scarpulla. Sudden Changes Detection in WCE Video. In P. Foggia, C. Sansone, and M. Vento, editors, Image Analysis and Processing -ICIAP 2009, volume 5716 of Lecture Notes in Computer Science, pages 701-710. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009. open in new tab
  52. G. Gallo, E. Granata, and A. Torrisi. Information Theory Based WCE Video Summarization. In 2010 20th International Conference on Pattern Recognition, pages 4198-4201. IEEE, 2010. open in new tab
  53. J. Gama. A survey on learning from data streams: current and future trends. Progress in Artificial Intelligence, 1(1):45-55, apr 2012. open in new tab
  54. Y. Gaol, W. Tavanapongl, K. Kim, J. Wong, J. Oh, and P. C. D. Groen. A framework for parsing colonoscopy videos for semantic units. Gastroenterology And Hepatology, pages 1879-1882, 2004.
  55. U. Gargi, R. Kasturi, and S. Strayer. Performance characterization of video-shot-change detection methods. IEEE Transactions on Circuits and Systems for Video Technology, 10(1):1-13, 2000. open in new tab
  56. J. M. Gauch, S. Gauch, S. Bouix, and X. Zhu. Real time video scene detection and classification. Information Processing & Management, 35(3):381-400, 1999. open in new tab
  57. M. Grundmann, V. Kwatra, M. Han, and I. Essa. Efficient hierarchical graph-based video segmentation. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2141-2148. IEEE, jun 2010. open in new tab
  58. Guodong Guo, Hong-Jiang Zhang, and S. Li. Distance-from-boundary as a metric for texture image retrieval. In 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), volume 3, pages 1629-1632. IEEE, 2001.
  59. M. Gupta, J. Gao, C. C. Aggarwal, and J. Han. Outlier Detection for Temporal Data tutorial. In 2013 SIAM International Conference on Data Mining, Austin, Texas, USA, 2013. open in new tab
  60. M. Gupta, J. Gao, C. C. Aggarwal, and J. Han. Outlier Detection for Temporal Data: A Survey. IEEE Transactions on Knowledge and Data Engineering, 26(9):2250-2267, sep 2014. open in new tab
  61. A. Hadid and M. Pietikainen. From still image to video-based face recognition: an experimental analysis. In Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., pages 813-818. IEEE, 2004. open in new tab
  62. M. Häfner, M. Liedlgruber, A. Uhl, A. Vécsei, and F. Wrba. Color treatment in endoscopic image classifi- cation using multi-scale local color vector patterns. Medical Image Analysis, 16(1):75-86, 2012. open in new tab
  63. O. Haji-Maghsoudi, A. Talebpour, H. Soltanian-Zadeh, and N. Haji-maghsoodi. Automatic organs' detection in WCE. In The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), pages 116-121. IEEE, may 2012. open in new tab
  64. A. E. Hassanien, A. Abraham, J. F. Peters, G. Schaefer, and C. Henry. Rough sets and near sets in medical imaging: a review. IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society, 13(6):955-68, nov 2009. open in new tab
  65. K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778. IEEE, jun 2016. open in new tab
  66. D. J. Hill and B. S. Minsker. Anomaly detection in streaming environmental sensor data: A data-driven modeling approach. Environmental Modelling & Software, 25(9):1014-1022, sep 2010. open in new tab
  67. S. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735-1780, nov 1997. open in new tab
  68. X.-S. Hua, D. Zhang, M. Li, and H.-J. Zhang. Performance Evaluation Protocol for Video Scene Detection Algorithms. In Workshop on Multimedia Information Retrieval, in conjunction with 10th ACM Multimedia, 2002. open in new tab
  69. J. Huang, Z. Liu, and Y. Wang. Joint video scene segmentation and classification based on hidden Markov model. In 2000 IEEE International Conference on Multimedia and Expo, volume 3, pages 1551-1554. IEEE, 2000.
  70. D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge. Comparing Images Using the Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):850-863, 1993. open in new tab
  71. G. Iddan, G. Meron, A. Glukhovsky, and P. Swain. Wireless capsule endoscopy. Nature, 405(6785):417, may 2000. open in new tab
  72. Indrabayu, R. Y. Bakti, I. S. Areni, and A. A. Prayogi. Vehicle detection and tracking using Gaussian Mixture Model and Kalman Filter. In 2016 International Conference on Computational Intelligence and Cybernetics, pages 115-119. IEEE, 2016. open in new tab
  73. G. Iyengar and A. Lippman. Models for automatic classification of video sequences. In SPIE Proc. Storage and Retrieval for Image and Video Databases, pages 216-227, 1997. open in new tab
  74. D. Jacobs, D. Weinshall, and Y. Gdalyahu. Condensing image databases when retrieval is based on non- metric distances. In Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pages 596-601. Narosa Publishing House, 1998. open in new tab
  75. H. V. Jagadish, N. Koudas, and S. Muthukrishnan. Mining Deviants in a Time Series Database. In Pro- ceedings of the 25th International Conference on Very Large Data Bases, pages 102-113. Morgan Kaufmann Publishers Inc., sep 1999.
  76. Joe Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici. Beyond short snippets: Deep networks for video classification. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4694-4702. IEEE, jun 2015.
  77. J. F. Kaiser and W. A. Reed. Data smoothing using low-pass digital filters. Review of Scientific Instruments, 48(11):1447, 1977. open in new tab
  78. S. Kamkar and R. Safabakhsh. Vehicle detection, counting and classification in various conditions. IET Intelligent Transport Systems, 10(6):406-413, aug 2016. open in new tab
  79. A. Karargyris and N. Bourbakis. Wireless Capsule Endoscopy and Endoscopic Imaging: A Survey on Various Methodologies Presented. IEEE Engineering in Medicine and Biology Magazine, 29(1):72-83, 2010. open in new tab
  80. B. Kedem and K. Fokianos. Regression Theory for Categorical Time Series. Statistical Science, 18(3): 357-376, aug 2003. open in new tab
  81. B. Kedem and K. Fokianos. Regression Models for Time Series Analysis. 2005. open in new tab
  82. B. Kedem and K. Fokianos. Regression Models for Binary Time Series. In Modeling Uncertainty, pages 185-199. Kluwer Academic Publishers, Boston, 2005. open in new tab
  83. Y. Keller and A. Averbuch. Multisensor image registration via implicit similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5):794-801, may 2006. open in new tab
  84. V. S. Kodogiannis and M. G. Boulougoura. An Adaptive Neurofuzzy Approach for the Diagnosis in Wireless Capsule Endoscopy Imaging. International Journal of Information Technology, 13(1):46-56, 2007. open in new tab
  85. J. Komorowski, Z. Pawlak, L. Polkowski, and A. Skowron. Rough Sets: A Tutorial, 1998.
  86. I. Koprinska and S. Carrato. Temporal video segmentation: A survey. Signal Processing: Image Commu- nication, 16(5):477-500, jan 2001. open in new tab
  87. H. Krawczyk and J. Proficz. KASKADA -MULTIMEDIA PROCESSING PLATFORM ARCHITECTURE. In SIGMAP Conference Proceedings, 2010. open in new tab
  88. H. Krawczyk and J. Proficz. Real-Time Multimedia Stream Data Processing in a Supercomputer Environ- ment. In Interactive Multimedia. InTech, mar 2012. open in new tab
  89. Y. Lecun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436-444, 2015. open in new tab
  90. J. Lee, J. Oh, S. K. Shah, X. Yuan, and S. J. Tang. Automatic classification of digestive organs in wireless capsule endoscopy videos. Proceedings of the 2007 ACM symposium on Applied computing SAC 07, (c): 1041-1045, 2007. open in new tab
  91. B. Y. Li, A. S. Mian, W. Liu, and A. Krishna. Using Kinect for face recognition under varying poses, ex- pressions, illumination and disguise. In 2013 IEEE Workshop on Applications of Computer Vision (WACV), pages 186-192. IEEE, jan 2013. open in new tab
  92. Y. Li and R. L. Stevenson. A Similarity Metric for Multimodal Images Based on Modified Hausdorff Distance. In 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, pages 143-148. IEEE, sep 2012. open in new tab
  93. Y. Li, R. Wang, Z. Huang, S. Shan, and X. Chen. Face Video Retrieval With Image Query via Hashing Across Euclidean Space and Riemannian Manifold. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4758-4767, 2015. open in new tab
  94. M. Liedlgruber and A. Uhl. Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review. IEEE reviews in biomedical engineering, 4:73-88, 2011. open in new tab
  95. S. Lin. Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):555-570, sep 2010. open in new tab
  96. T. Lin and H.-j. Zhang. Automatic video scene extraction by shot grouping. Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 4:39-42, 2000.
  97. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal Loss for Dense Object Detection. arXiv preprint, arXiv:1708.02002, aug 2017. open in new tab
  98. H. Ling and K. Okada. EMD-L 1: An Efficient and Robust Algorithm for Comparing Histogram-Based Descriptors. Lecture Notes in Computer Science, 3953:330-343, 2006. open in new tab
  99. X. Ling, L. Chao, L. Huan, and X. Zhang. A General Method for Shot Boundary Detection. In 2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008), pages 394-397. IEEE, 2008. open in new tab
  100. P. Liskowski and K. Krawiec. Segmenting Retinal Blood Vessels With Deep Neural Networks. IEEE Transactions on Medical Imaging, 35(11):2369-2380, nov 2016. open in new tab
  101. D. Liu and T. Chen. Object Detection in Video with Graphical Models. In 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, volume 5, pages 693-696. IEEE, 2006. open in new tab
  102. M. Y. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa. Fast directional chamfer matching. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1696-1703, 2010. open in new tab
  103. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. SSD: Single Shot MultiBox Detector. In ECCV, pages 21-37. Springer, Cham, oct 2016. open in new tab
  104. Y. Liu, L. Zeng, and Y. Huang. An efficient HOG-ALBP feature for pedestrian detection. Signal, Image and Video Processing, 8(S1):125-134, dec 2014. open in new tab
  105. Y. Liu, S. Zhang, M. Xu, and X. He. Predicting Salient Face in Multiple-Face Videos. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3224-3232. IEEE, jul 2017. open in new tab
  106. B. Lovell and P. Kootsookos. Evaluation of HMM training algorithms for letter hand gesture recognition. In Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No.03EX795), pages 648-651. IEEE, 2003.
  107. Y. Lu, C. Lu, and C.-K. Tang. Online Video Object Detection Using Association LSTM. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2363-2371. IEEE, oct 2017. open in new tab
  108. J. Ma and S. Perkins. Time-series novelty detection using one-class support vector machines. In Proceedings of the International Joint Conference on Neural Networks, 2003., volume 3, pages 1741-1745. IEEE, 2003. open in new tab
  109. M. Mackiewicz. Capsule Endoscopy -State of the Technology and Computer Vision Tools After the First Decade. In O. Pascu and A. Seicean, editors, New Techniques in Gastrointestinal Endoscopy, chapter 7, pages 103-124. InTech, oct 2011. open in new tab
  110. M. Mackiewicz, J. Berens, and M. Fisher. Wireless capsule endoscopy color video segmentation. IEEE Transactions on Medical Imaging, 27(12):1769-1781, 2008. open in new tab
  111. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16(2):187-198, apr 1997. open in new tab
  112. G. D. Magoulas. Neuronal networks and textural descriptors for automated tissue classification in endoscopy. Oncology reports, 15 Spec no:997-1000, 2006. open in new tab
  113. G. D. Magoulas, V. P. Plagianakos, and M. N. Vrahatis. Neural network-based colonoscopic diagnosis using on-line learning and differential evolution. Applied Soft Computing, 4(4):369-379, sep 2004. open in new tab
  114. B. Mahasseni, M. Lam, and S. Todorovic. Unsupervised Video Summarization with Adversarial LSTM Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2982- 2991. IEEE, jul 2017. open in new tab
  115. P. Marchand and L. Marmet. Binomial smoothing filter: A way to avoid some pitfalls of least-squares polynomial smoothing. Review of Scientific Instruments, 54(8):1034, 1983. open in new tab
  116. M. Q.-H. Meng and B. Li. Tumor CE image classification using SVM-based feature selection. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1322-1327. IEEE, oct 2010.
  117. T. Mitchell. Machine Learning. McGraw-Hill, 1997. open in new tab
  118. G. D. F. Morales and A. Bifet. SAMOA: Scalable Advanced Massive Online Analysis. Journal of Machine Learning Research, 16(Jan):149-153, 2015.
  119. B. Munzer, K. Schoeffmann, and L. Boszormenyi. Detection of Circular Content Area in Endoscopic Videos for Efficient Encoding and Improved Content Analysis. Technical report, Institute of Information Technology, University Klagenfurt, 2012. open in new tab
  120. B. Munzer, K. Schoeffmann, and L. Boszormenyi. Detection of circular content area in endoscopic videos. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pages 534-536. IEEE, jun 2013. open in new tab
  121. T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture clas- sification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (7):971-987, jul 2002. open in new tab
  122. T. Ojala, M. Pietikäinen, and T. Mäenpää. Multiresolution gray-scale and rotation invariant texture clas- sification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (7):971-987, 2002. open in new tab
  123. G. Pan and L. Wang. Swallowable Wireless Capsule Endoscopy: Progress and Technical Challenges. Gas- troenterology Research and Practice, 2012. open in new tab
  124. Z. Pawlak. On Some Issues Connected With Roughly Continuous Functions. 1995. open in new tab
  125. Z. Pawlak. Rough calculus. Technical Report 58, 1995. open in new tab
  126. W. Pei, T. Baltrušaitis, D. M. J. Tax, and L.-P. Morency. Temporal Attention-Gated Model for Robust Sequence Classification. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 820-829. IEEE, dec 2017. open in new tab
  127. Y. Poleg, T. Halperin, C. Arora, and S. Peleg. EgoSampling: Fast-Forward and Stereo for Egocentric Videos. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4768-4776, 2015. open in new tab
  128. L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286, 1989. open in new tab
  129. V. P. Rainer Lienhart, Er Kuranov. Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. In In DAGM 25th Pattern Recognition Symposium, pages 297-304, 2003.
  130. M. Ramona, G. Richard, and B. David. Vocal detection in music with support vector machines. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 1885-1888. IEEE, mar 2008. open in new tab
  131. J. Redmon and A. Farhadi. YOLO9000: Better, Faster, Stronger. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6517-6525. IEEE, dec 2017. open in new tab
  132. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779-788, jun 2016. open in new tab
  133. S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137-1149, jun 2017. open in new tab
  134. F. Riaz, F. B. Silva, M. D. Ribeiro, and M. T. Coimbra. Invariant Gabor texture descriptors for classification of gastroenterology images. IEEE transactions on bio-medical engineering, 59(10):2893-904, oct 2012. open in new tab
  135. G. Q. Rosa Ruiloba, Stephane March. Towards a Standard Protocol for the Evaluation of Video-to-Shots Segmentation Algorithms. In First European Workshop on Content-Based Multimedia Indexing, 1999.
  136. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. ORB: An efficient alternative to SIFT or SURF. In 2011 International Conference on Computer Vision, pages 2564-2571. IEEE, nov 2011. open in new tab
  137. Y. Rubner, C. Tomasi, and L. J. Guibas. The Earth Mover's Distance as a Metric for Image Retrieval. International Journal of Computer Vision, 40(2):99-121, 2000. open in new tab
  138. D. B. Russakoff, C. Tomasi, T. Rohlfing, and C. R. Maurer. Image Similarity Using Mutual Information of Regions. In T. Pajdla and J. Matas, editors, Computer Vision -ECCV 2004, pages 596-607, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg. open in new tab
  139. S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach (3rd Edition). Prentice Hall, 2009.
  140. U. Sakarya and Z. Telatar. Video scene detection using graph-based representations. Signal Processing: Image Communication, 25(10):774-783, nov 2010. open in new tab
  141. V. Stanisavljevic, Z. Kalafatic, and S. Ribaric. Optical flow estimation over extended image sequence. In 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099), volume 2, pages 546-549. IEEE, 2000. open in new tab
  142. B. Stenger, A. Thayananthan, P. Torr, and R. Cipolla. Estimating 3D hand pose using hierarchical multi- label classification. Image and Vision Computing, 25(12):1885-1894, dec 2007. open in new tab
  143. R. Sukthankar and M. Hebert. Efficient visual event detection using volumetric features. In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, volume 1, pages 166-173 Vol. 1. IEEE, 2005.
  144. J. Sun, J. Wang, and T.-C. Yeh. Video Understanding: From Video Classification to Captioning. Technical report, Stanford University, 2017.
  145. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. In AAAI Conference on Artificial Intelligence, San Francisco, feb 2017. open in new tab
  146. M. Teutsch and W. Kruger. Robust and fast detection of moving vehicles in aerial videos using sliding windows. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 26-34. IEEE, jun 2015. open in new tab
  147. That Mon Htwe, Chee Khun Poh, Liyuan Li, Jiang Liu, Eng Hui Ong, and Khek Yu Ho. Vision-based techniques for efficient Wireless Capsule Endoscopy examination. In 2011 Defense Science Research Con- ference and Expo (DSR), pages 1-4. Department of Computer Vision and Image Understanding, Institute for Infocomm Research, Singapore 138632, IEEE, aug 2011. open in new tab
  148. K.-L. Ton-Thi, T.-A. Nguyen, and M.-C. Hong. Video stabilization algorithm using a moving alpha-trimmed mean filter window. In The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014), pages 1-2. IEEE, jun 2014. open in new tab
  149. G. V. Trunk. A Problem of Dimensionality: A Simple Example. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(3):306-307, jul 1979. open in new tab
  150. S. Tsevas, D. K. Iakovidis, D. Maroulis, and E. Pavlakis. Automatic frame reduction of Wireless Capsule Endoscopy video. In 2008 8th IEEE International Conference on BioInformatics and BioEngineering, pages 1-6. IEEE, 2008. open in new tab
  151. P. Turaga. Statistical and Geometric Modeling of Spatio-Temporal Patterns for Video Understanding. PhD thesis, University of Maryland, 2009.
  152. K. Uosaki and P. Statement. Integer programming approach to optimal smoothing of two-state Markov sequences. In ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 11, pages 1661-1664. Institute of Electrical and Electronics Engineers, 1986. open in new tab
  153. J. Vendrig and M. Worring. Systematic evaluation of logical story unit segmentation. IEEE Transactions on Multimedia, 4(4):492-499, dec 2002. open in new tab
  154. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 511-518. IEEE Comput. Soc, 2001. open in new tab
  155. P. Viola and M. Jones. Robust Real-time Object Detection. International Journal of Computer Vision, 2001. open in new tab
  156. P. Viola and W. M. Wells III. Alignment by Maximization of Mutual Information. International Journal of Computer Vision, 24(2):137-154, 1997. open in new tab
  157. H. Vu, T. Echigo, R. Sagawa, K. Yagi, M. Shiba, K. Higuchi, T. Arakawa, and Y. Yagi. Contraction detection in small bowel from an image sequence of wireless capsule endoscopy. Medical Image Computing and Computer-Assisted Intervention, 10(Pt 1):775-783, 2007. open in new tab
  158. H. Wang, S. Zhang, W. Liang, F. Wang, and Y. Yao. Content-based image retrieval using fractional distance metric. In 2012 International Conference on Image Analysis and Signal Processing, pages 1-5. IEEE, nov 2012. open in new tab
  159. X. Wang and X.-P. Zhang. An ICA Mixture Hidden Conditional Random Field Model for Video Event Classification. IEEE Transactions on Circuits and Systems for Video Technology, 23(1):46-59, jan 2013. open in new tab
  160. Y. Wang, X. Xu, and Z. Yu. Notes for rough derivatives and rough continuity in rough function model. In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pages 245-247. IEEE, aug 2010. open in new tab
  161. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4):600-612, apr 2004. open in new tab
  162. J. Weber, S. Lefevre, and P. Gancarski. Video Object Mining: Issues and Perspectives. In 2010 IEEE Fourth International Conference on Semantic Computing, pages 85-90. IEEE, sep 2010. open in new tab
  163. Y. Wei, S. M. Bhandarkar, and K. Li. Semantics-Based Video Indexing using a Stochastic Modeling Approach. In 2007 IEEE International Conference on Image Processing, volume 4, pages IV -313-IV - 316. IEEE, 2007. open in new tab
  164. Y. Wong, S. Chen, S. Mau, C. Sanderson, and B. C. Lovell. Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In CVPR 2011 Workshops, pages 74-81. IEEE, jun 2011. open in new tab
  165. S. Wu and J. Yang. Local Image Distance Metric Learning. In 2010 Chinese Conference on Pattern Recognition (CCPR), pages 1-5. IEEE, oct 2010. open in new tab
  166. L. Xie, I. B. M. T. J. Watson, and S.-f. Chang. Pattern Mining in Visual Concept Streams. In 2006 IEEE International Conference on Multimedia and Expo, number 1, pages 297-300. IEEE, jul 2006. open in new tab
  167. F. Xiong, X. Shi, and D.-Y. Yeung. Spatiotemporal Modeling for Crowd Counting in Videos. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 5161-5169. IEEE, oct 2017. open in new tab
  168. W. Xiong and J. C.-M. Lee. Efficient Scene Change Detection and Camera Motion Annotation for Video Classification. Computer Vision and Image Understanding, 71(2):166-181, aug 1998. open in new tab
  169. Y. Yang, B. C. Lovell, and F. Dadgostar. Content-Based Video Retrieval (CBVR) System for CCTV Surveillance Videos. In 2009 Digital Image Computing: Techniques and Applications, pages 183-187. IEEE, 2009. open in new tab
  170. J. Yu, J. Amores, N. Sebe, and Q. Tian. A New Study on Distance Metrics as Similarity Measurement. In 2006 IEEE International Conference on Multimedia and Expo, pages 533-536. IEEE, jul 2006. open in new tab
  171. J. Zhang. An improved clustering for action recognition in online video. In 2011 International Conference on Multimedia Technology, pages 180-183. Ieee, jul 2011.
  172. Z. Zhang. Microsoft Kinect Sensor and Its Effect. IEEE Multimedia, 19(2):4-10, feb 2012. open in new tab
  173. Q. Zhao and M. Q. Meng. An abnormality based WCE video segmentation strategy. In 2010 IEEE International Conference on Automation and Logistics, ICAL 2010, pages 565-570. IEEE, aug 2010.
  174. Q. Zhao and M. Q.-H. Meng. WCE video abstracting based on novel color and texture features. In 2011 IEEE International Conference on Robotics and Biomimetics, pages 455-459. IEEE, dec 2011. open in new tab
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