Abstract
Visual content searching, browsing and retrieval tools have been a focus area of interest as they are required by systems from many different domains. Context-based, Content-Based, and Semantic-based are different approaches utilized for indexing/retrieving, but have their drawbacks when applied to systems that aim to mimic the human capabilities. Such systems, also known as Cognitive Systems, are still limited in terms of processing different sources of information (especially when structured in different ways) for decision making purposes. This issue becomes significantly greater when past information is retrieved and taken in account. We address this issue by proposing a Structuralized Context-Aware Indexing and Retrieval using Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). SOEKS and DDNA allow the creation of a multi-modal space composed of information from different sources, such as contextual, visual, auditory etc., in a form of a structure and explicit experiential knowledge. SOKES is composed by fields that allow this experiences to participate in the processes of similarity, uncertainty, impreciseness, or incompleteness measures and facilitate the indexing and retrieval of knowledge in Cognitive Systems.
Citations
-
3
CrossRef
-
0
Web of Science
-
4
Scopus
Authors (3)
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
- Copyright (Springer Nature Switzerland AG 2020)
Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Published in:
-
Advances in Intelligent Systems and Computing
pages 7 - 16,
ISSN: 2194-5357 - Title of issue:
- Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019 strony 7 - 16
- Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- De Silva Oliveira C., Sanin C., Szczerbicki E.: Context-Aware Indexing and Retrieval for Cognitive Systems Using SOEKS and DDNA// Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019/ ed. Leszek Borzemski, Jerzy Świątek,Zofia Wilimowska : Springer , 2019, s.7-16
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-030-30440-9_2
- Bibliography: test
-
- Sanin, C., Haoxi, Z., Shafiq, I., Waris, M. M., de Oliveira, C. S., & Szczerbicki, E. (2018). Experience based knowledge representation for Internet of Things and Cyber Physical Sys- tems with case studies. Future Generation Computer Systems. open in new tab
- Vernon, D.: The space of cognitive vision. In Cognitive Vision Systems, 7-24, Springer, Berlin, Heidelberg (2006). open in new tab
- Gregory, R. L. (1973). Eye and brain: The psychology of seeing. McGraw-Hill. open in new tab
- S. Malik and S. Jain, "Ontology based context aware model," 2017 International Confer- ence on Computational Intelligence in Data Science(ICCIDS), Chennai, 2017, pp. 1-6. doi: 10.1109/ICCIDS.2017.8272632 open in new tab
- U. Manzoor, N. Ejaz, N. Akhtar, M. Umar, M. S. Khan and H. Umar, "Ontology based im- age retrieval," 2012 International Conference for Internet Technology and Secured Trans- actions, London, 2012, pp. 288-293. open in new tab
- C. Sanin, E. Szczerbicki, Experience-based Knowledge Representation SOEKS. Cybernet Sys. 40(2) (2009) 99-122. open in new tab
- Sanin, C., Toro, C., Haoxi, Z., Sanchez, E., Szczerbicki, E., Carrasco, E.,. & Man-cilla- Amaya, L. (2012). Decisional DNA: A multi-technology shareable knowledge structure for decisional experience. Neurocomputing, 88, 42-53. open in new tab
- M. De Marsicoi, L. Cinque, S. Levialdi, Indexing pictorial documents by their content: A survey of current techniques, Image and Vision Computing 15 (1997) 119-141. open in new tab
- Y. Rui, T. Huang, S. Chang, Image retrieval Past, present, and future, in: International Symposium on Multimedia Information Processing.
- Y. Rui, T. Huang, S. Chang, Image retrieval: current techniques, promising directions and open issues, Journal of Visual Communication and Image Representation, pp. 39-62. open in new tab
- D.B. H. Muller, N. Michoux, A. Geissbuhler, A review of content-based image retrieval systems in medical applications clinical benefits and future directions, International Jour- nal of Medical Informatics 73 (2004) 1-23. open in new tab
- Westerveld, T. (2000, April). Image retrieval: Content versus context. In Content-Based Multimedia Information Access-Volume 1 (pp. 276-284).
- Raveaux, R., Burie, J. C., & Ogier, J. M. (2013). Structured representations in a content based image retrieval context. Journal of Visual Communication and Image Representa- tion, 24(8), 1252-1268. open in new tab
- Alkhawlani, M., Elmogy, M., & El Bakry, H. (2015). Text-based, content-based, and se- mantic-based image retrievals: A survey. Int. J. Comput. Inf. Technol, 4(01). open in new tab
- H. Tamura and N. Yokoya, "Image database systems: A survey," Pattern recognition, vol. 17, 1984, pp. 29-43. open in new tab
- Oard, D.W. and Dorr, B.J. (1996) A Survey of Multilingual Text Retrieval. Technical Re- port UMIACS-TR-96-19, University of Maryland, Institute for Advanced Computer Stud- ies. open in new tab
- S.H. Liu, S.K. Chang, Picture indexing and abstraction techniques for pictorial databases, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 6 (4) (1984) 475-483.
- R. Datta, D. Joshi, J. Li, James, Z. Wang, Image retrieval: Ideas, influences, and trends of the new age, ACM Computing Surveys 39 (2006) 2007 open in new tab
- Danielsson, P. E. (1980). Euclidean distance mapping. Computer Graphics and image pro- cessing, 14(3), 227-248. open in new tab
- H.H. Wang, D. Mohamad, and N. Ismail, "Image Retrieval: Techniques, Challenge, and Trend," International conference on Machine Vision, Image processing and Pattern Analy- sis, Bangkok, Citeseer, 2009. open in new tab
- N. Shanmugapriya and R. Nallusamy, "Anew content based image retrieval system using GMM and relevance feedback," Journal of Computer Science 10 (2): 330-340, 2013. open in new tab
- Gorkani, M. M., & Picard, R. W. (1994, October). Texture orientation for sorting photos" at a glance". In International conference on Pattern recognition (pp. 459-459). open in new tab
- Yiu, E. C. (1996). Image classification using color cues and texture orientation (Doctoral dissertation, Massachusetts Institute of Technology).
- Zhu, S. C., Wu, Y., & Mumford, D. (1998). Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling. International Journal of Com- puter Vision, 27(2), 107-126.
- Zin, N. A. M., Yusof, R., Lashari, S. A., Mustapha, A., Senan, N., & Ibrahim, R. (2018, June). Content-Based Image Retrieval in Medical Domain: A Review. In Journal of Phys- ics: Conference Series (Vol. 1019, No. 1, p. 012044). IOP Publishing.
- Bandura, A. (1989). Human agency in social cognitive theory. American psycholo- gist, 44(9), 1175. open in new tab
- Hollnagel, E., & Woods, D. D. (2005). Joint cognitive systems: Foundations of cognitive systems engineering. CRC Press. open in new tab
- J. Amores, N. Sebe and P. Radeva, "Context-Based Object-Class Recognition and Retriev- al by Generalized Correlograms," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 1818-1833, Oct. 2007. open in new tab
- Sanin, C., Toro, C., Haoxi, Z., Sanchez, E., Szczerbicki, E., Carrasco, E.,. & Man-cilla- Amaya, L. (2012). Decisional DNA: A multi-technology shareable knowledge structure for decisional experience. Neurocomputing, 88, 42-53. open in new tab
- de Oliveira, C. S., Sanin, C., & Szczerbicki, E. (2019). Visual Content Learning in a Cog- nitive Vision Platform for Hazard Control (CVP-HC). Cybernetics and Systems, 50(2), 197-207.
- de Oliveira, C. S., Sanin, C., & Szczerbicki, E. (2019, April). Towards Knowledge For- malization and Sharing in a Cognitive Vision Platform for Hazard Control (CVP-HC). open in new tab
- In Asian Conference on Intelligent Information and Database Systems (pp. 53-61). Spring- er, Cham. open in new tab
- Deserno, T. M., Antani, S., & Long, R. (2009). Ontology of gaps in content-based image retrieval. Journal of digital imaging, 22(2), 202-215. open in new tab
- Sanin, C., Szczerbicki, E.: Using XML for implementing set of experience knowledge structure. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3681, pp. 946-952. Springer, Heidelberg (2005). open in new tab
- Sanín, C. A. M. (2007). Smart knowledge management system. University of Newcastle. open in new tab
- Wang, P., Sanin, C., & Szczerbicki, E. (2012). Enhancing Set of Experience Knowledge Structure (SOEKS) WITH A Nearest Neighbor Algorithm RELIE-F. Information Systems Architecture and Technology, 13.
- Verified by:
- Gdańsk University of Technology
seen 125 times
Recommended for you
Image Representation for Cognitive Systems Using SOEKS and DDNA: A Case Study for PPE Compliance
- C. Silva de Oliveira,
- C. Sanin,
- E. Szczerbicki
Smart Knowledge Engineering for Cognitive Systems: A Brief Overview
- C. Silva de Oliveira,
- C. Sanin,
- E. Szczerbicki
Gaining knowledge through experience: developing decisional DNA applications in robotics
- H. Zhang,
- C. Sanin,
- E. Szczerbicki