Abstract
We propose a robust object recognition system where patch-based pyramid images and the spatial relationships among patches are utilized for our image model. In particular, both a color histogram (CH) and a color co-occurrence histogram (CCH) are applied to obtain image features for each patch. The locations of subregions to be tested are decided by a particle filter in our matching process. We show that the performance of object recognition can be improved by using the spatial relationships among patches. To show the validity of our proposed method, we employ input images from various environments as test images.
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Chang P, Krumm J (1999) Object recognition with color cooccurrence histograms. IEEE Conference on CVPR 1999, Fort Collins, CO, USA, June 23–25, p 2498
Redfield S, Nechyba M, Harris JG, et al (2001) Efficient object recognition using color. Florida Conference on Recent Advances in Robotics, Boca Raton
Zhang W, Deng H, Dietterich TG, et al (2006) A hierarchical object recognition system based on multi-scale principal curvature regions. In: Proceedings of the 18th International Conference of Pattern Recognition (ICPR’06) 2006, pp 778–782
Hegerath A, Deselaers T, Ney H (2006) Patch-based object recognition using discriminatively trained Gaussian mixtures. BMVC 2006, pp 519–528
Ahmadyfard A, Kittler J (2000) Region-based representation for object recognition by relaxation labeling. LNCS 1876, pp 297–307
Wang X, Keller JM, Gader P (1997) Using spatial relationship as features in object recognition. NAFIPS 1997, pp 160–165
Bouchard G, Triggs B (2005) Hierarchical part-based visual object categorization. Proceedings of the 2005 IEEE CVPR, pp 710–715
Epshtein B, Ullman S (2005) Feature hierarchies for object classification. ICCV 2005, pp 220–207
Crandall DJ, Huttenlocher DP (2006) Weakly supervised learning of part-based spatial models for visual object recognition. LNCS 3951, pp 16–29
Pham TV, Smeulders AWM (2006) Learning spatial relations in object recognition. Pattern Recognition Lett 27:1673–1684
Ohba K, Sato Y, Ikeuku K (2000) Appearance-based visual learning and object recognition with illumination invariance.Mach Vision Appl pp 189–196
Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. The MIT, Cambridge, MA, pp 85–116
Wu A, Xu D, Yang X, et al (2005) Generic solution for image object recognition based on vision cognition theory. Fuzzy System Knowledge Discovery, vol 3614, pp 1265–1275
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This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008
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Bang, H., Lee, S., Yu, D. et al. Robust object recognition using a color co-occurrence histogram and the spatial relations of image patches. Artif Life Robotics 13, 488–492 (2009). https://doi.org/10.1007/s10015-008-0614-5
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DOI: https://doi.org/10.1007/s10015-008-0614-5