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Abstract
A comparative analysis of various visual descriptors is presented in this chapter. The descriptors utilize many aspects of image data: colour, texture, gradient, and statistical moments. The descriptor list is supplemented with local features calculated in close vicinity of key points found automatically in the image. The goal of the analysis is to find descriptors that are best suited for particular task, i.e. re-identification of objects in a multi-camera environment. The analysis is performed using two datasets containing images of humans and vehicles recorded with different cameras. For the purpose of descriptor evaluation, scatter and clustering measures are supplemented with a new measure that is derived from calculating direct dissimilarities between pairs of images. In order to draw conclusions from multi-dataset analysis, four aggregation measures are introduced. They are meant to find descriptors that provide the best identification effectiveness, based on the relative ranking, and simultaneously are characterized with large stability (invariance to the selection of objects in the dataset). Proposed descriptors are evaluated practically with object re-identification experiments involving four classifiers to detect the same object after its transition between cameras’ fields of view. The achieved results are discussed in detail and illustrated with figures.
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Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- W : Feature Selection for Data and Pattern Recognition strony 263 - 303
- Language:
- English
- Publication year:
- 2014
- Bibliographic description:
- Dalka P., Ellwart D., Szwoch G., Lisowski K., Szczuko P., Czyżewski A.: Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification// W : Feature Selection for Data and Pattern Recognition/ ed. Stańczyk, Urszula, Jain, Lakhmi C. Berlin: Springer, 2014, s.263-303
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-662-45620-0_12
- Verified by:
- Gdańsk University of Technology
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