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2015 | OriginalPaper | Buchkapitel

12. Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification

verfasst von : Piotr Dalka, Damian Ellwart, Grzegorz Szwoch, Karol Lisowski, Piotr Szczuko, Andrzej Czyżewski

Erschienen in: Feature Selection for Data and Pattern Recognition

Verlag: Springer Berlin Heidelberg

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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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Allen, R., Mcgeorge, P., Pearson, D., Milne, A.B.: Attention and expertise in multiple target tracking. Appl. Cogn. Psychol. 18(3), 337–347 (2004)CrossRef

Antani, S., Kasturi, R., Jain, R.: A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recognit. 35(4), 945–965 (2002)CrossRefMATH

Bannour, H., Hlaoua, L., El Ayeb, B.: Survey of the adequate descriptor for content-based image retrieval on the web: global versus local features. In: Conference en Recherche d’Information et Applications CORIA, pp. 445–456. LSIS-USTV (2009)

Baxes, G.A.: Digital Image Processing: Principles and Applications. Wiley, New York (1994)

Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRef

Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefMATH

Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Chapman and Hall, New York (1994)

Burger, W., Burge, M.J.: Principles of Digital Image Processing: Core Algorithms. Springer, Berlin (2009)

Burger, W., Burge, M.J.: Principles of Digital Image Processing: Fundamental Techniques. Springer, New York (2009)

10.

Cavanagh, P., Alvarez, G.A.: Tracking multiple targets with multifocal attention. Trends Cogn. Sci. 9(7), 349–354 (2005)CrossRef

11.

Chang-yeon, J.: Face detection using LBP features. Final project report—CS 229 machine learning (2008)

12.

Cipolla, R., Battiato, S., Farinella, G.: Computer Vision: Detection, Recognition and Reconstruction. Studies in Computational Intelligence. Springer, New York (2010)CrossRef

13.

Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28(1), 45–62 (2002)CrossRef

14.

Czyżewski, A., Lisowski, K.: Employing flowgraphs for forward route reconstruction in video surveillance system. J. Intell. Inf. Syst. 40, 1–15 (2013)CrossRef

15.

Dalka, P., Szwoch, G., Ciarkowski, A.: Distributed framework for visual event detection in parking lot area. In: Dziech, A., Czyżewski, A. (eds.) Multimedia Communications, Services and Security. Communications in Computer and Information Science, vol. 149, pp. 37–45. Springer, Berlin (2011)

16.

Dalka, P., Szwoch, G., Szczuko, P., Czyżewski, A.: Video content analysis in the Urban area telemonitoring system. In: Tsihrintzis, G.A., Jain, L.C. (eds.) Multimedia Services in Intelligent Environments, Smart Innovation, Systems and Technologies, vol. 3, pp. 241–261. Springer, Berlin (2010)CrossRef

17.

Doyle, A., Lippert, R., Lyon, D.: Eyes Everywhere : The Global Growth of Camera Surveillance. Routledge, London (2012)

18.

Ellis, T.J., Makris, D., Black, J.K.: Learning a multi-camera topology. In: Proceedings of Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 165–171 (2003)

19.

Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2000)CrossRef

20.

Geng, X., Wang, L., Li, M., Wu, Q., Smith-Miles, K.: Adaptive fusion of gait and face for human identification in video. In: Proceedings of IEEE Workshop on Applications of Computer Vision WACV, pp. 1–6 (2008)

21.

Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc., Upper Saddle River (2006)

22.

Halkidi, M., Vazirgiannis, M., Batistakis, Y.: Quality scheme assessment in the clustering process. In: Zighed, D.A., Komorowski, J., Zytkow, J. (eds.) Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science, vol. 1910, pp. 265–276. Springer, Berlin (2000)CrossRef

23.

Hamdoun, O., Moutarde, F., Stanciulescu, B., Steux, B.: Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: Proceedings of the 2nd ACM/IEEE International Conference on Distributed Smart Cameras ICDSC, pp. 1–6 (2008)

24.

Hanbury, A., Kandaswamy, U., Adjeroh, D.A.: Illumination-invariant morphological texture classification. In: Ronse, C., Najman, L., Decencière, E. (eds.) 40 Years On Mathematical Morphology. Computational Imaging and Vision, vol. 30, pp. 377–386. Springer, Netherlands (2005)CrossRef

25.

Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC–3(6), 610–621 (1973)CrossRef

26.

Ilyas, A., Scuturici, M., Miguet, S.: Inter-camera color calibration for object re-identification and tracking. In: Proceedings of International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 188–193 (2010)

27.

Jeong, K., Jaynes, C.: Object matching in disjoint cameras using a color transfer approach. Mach. Vis. Appl. 19(5–6), 443–455 (2008)CrossRefMATH

28.

Kale, K.: Advances in Computer Vision and Information Technology. I.K. International Publishing House Pvt. Limited, New Delhi (2008)

29.

Kettnaker, V., Zabih, R.: Bayesian multi-camera surveillance. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 259 (1999)

30.

Kim, H., Romberg, J., Wolf, W.: Multi-camera tracking on a graph using Markov chain Monte Carlo. In: Proceedings of the 3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC, pp. 1–8 (2009)

31.

Kramer, A.F., Hahn, S.: Splitting the beam: distribution of attention over noncontiguous regions of the visual field. Psychol. Sci. 6(6), 381–386 (1995)CrossRef

32.

Li, J.H., Liu, M.S., Song, P.: An novel modified extraction method of MPEG-7 visual descriptor for image retrieval. In: Proceedings of International Conference on Machine Learning and Cybernetics (ICMLC), vol. 4, pp. 2037–2041 (2010)

33.

Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: Proceedings of IEEE 10th International Conference on Data Mining (ICDM), pp. 911–916 (2010)

34.

Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef

35.

Manjunath, B., Ohm, J.R., Vasudevan, V., Yamada, A.: Color and texture descriptors. IEEE Trans. Circuits Syst. Video Technol. 11(6), 703–715 (2001)CrossRef

36.

Martinez, J.M.: MPEG-7 overview. http://www.chiariglione.org/mpeg/standards/mpeg-7/mpeg-7.htm (2004)

37.

Mindru, F., Tuytelaars, T., Gool, L.V.: Moment invariants for recognition under changing viewpoint and illumination. Comput. Vis. Image Underst. 94(1–3), 3–27 (2004). Special Issue: Colour for Image Indexing and Retrieval

38.

Mittal, A., Davis, L.: Unified multi-camera detection and tracking using region-matching. In: Proceedings of IEEE Workshop on Multi-Object Tracking, pp. 3–10 (2001)

39.

Orr, G., Muller, K.: Neural Networks: Tricks of the Trade. Springer, New York (1998)CrossRef

40.

Piciarelli, C., Foresti, G.: Surveillance-oriented event detection in video streams. IEEE Intell. Syst. 26(3), 32–41 (2011)CrossRef

41.

Pinheiro, A.: Image descriptors based on the edge orientation. In: Proceedings of the 4th International Workshop on Semantic Media Adaptation and Personalization SMAP, pp. 73–78 (2009)

42.

Pitas, I.: Digital Image Processing Algorithms and Applications. Wiley-Interscience Publication, New York (2000)

43.

Sharma, S.: Applied multivariate techniques. Wiley, New York (1996)

44.

Spyrou, E., Borgne, H.L., Mailis, T., Cooke, E.: Fusing MPEG-7 visual descriptors for image classification. In: Proceedings of International Conference on Artificial Neural Networks (ICANN), pp. 847–852. Springer (2005)

45.

Szeliski, R.: Computer Vision: Algorithms and Applications. Texts in Computer Science. Springer, Heidelberg (2010)

46.

Szwoch, G., Dalka, P., Czyżewski, A.: A framework for automatic detection of abandoned luggage in airport terminal. In: Tsihrintzis, G., Damiani, E., Virvou, M., Howlett, R., Jain, L.C. (eds.) Intelligent Interactive Multimedia Systems and Services, Smart Innovation, Systems and Technologies, vol. 6, pp. 13–22. Springer, Berlin (2010)CrossRef

47.

Teixeira, L.F., Corte-Real, L.: Video object matching across multiple independent views using local descriptors and adaptive learning. Pattern Recognit. Lett. 30(2), 157–167 (2009)CrossRef

48.

Tian, Y.L., Hampapur, A., Brown, L., Feris, R., Lu, M., Senior, A., Shu, C.F., Zhai, Y.: Event detection, query, and retrieval for video surveillance, Chap. Artificial Intelligence for Max-imizing Content Based Image Retrieval, Information Science Reference (2008)

49.

Van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)CrossRef

50.

Wallraven, C., Caputo, B., Graf, A.: Recognition with local features: the kernel recipe. In: Proceedings of the 9th IEEE International Conference on Computer Vision, vol. 1, pp. 257–264 (2003)

Titel: Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification
verfasst von: Piotr Dalka
Damian Ellwart
Grzegorz Szwoch
Karol Lisowski
Piotr Szczuko
Andrzej Czyżewski
Verlag: Springer Berlin Heidelberg
Buch: Feature Selection for Data and Pattern Recognition
Print ISBN: 978-3-662-45619-4

Electronic ISBN: 978-3-662-45620-0

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-662-45620-0_12

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