



@article {2312,
	title = {Contextual classification of image patches with latent aspect models},
	journal = {Eurasip Journal on Image and Video ProcessingEurasip J. Image Video Process.},
	volume = {2009},
	year = {2009},
	note = {Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusArt. No.: 602920doi: 10.1155/2009/602920Language of Original Document: EnglishCorrespondence Address: Monay, F.; Idiap Research Institute Martigny, 1920 Martigny, Switzerland; email: florent.monay@idiap.chReferences: Kumar, S., Hebert, M., Discriminative random fields: A discriminative framework for contextual interaction in classification, 2, pp. 1150-1157. , Proceedings of the IEEE International Conference on Computer Vision (ICCV 03) October 2003 Nice, France;Lazebnik, S., Schmid, C., Ponce, J., Affine-invariant local descriptors and neighborhood statistics for texture recognition, 1, pp. 649-655. , Proceedings of the IEEE International Conference on Computer Vision (ICCV 03) October 2003 Nice, France; 
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},
	month = {2009///},
	abstract = {We present a novel approach for contextual classification of image patches in complex visual scenes, based on the use of histograms of quantized features and probabilistic aspect models. Our approach uses context in two ways: (1) by using the fact that specific learned aspects correlate with the semantic classes, which resolves some cases of visual polysemy often present in patch-based representations, and (2) by formalizing the notion that scene context is image-specificwhat an individual patch represents depends on what the rest of the patches in the same image are. We demonstrate the validity of our approach on a man-made versus natural patch classification problem. Experiments on an image collection of complex scenes show that the proposed approach improves region discrimination, producing satisfactory results and outperforming two noncontextual methods. Furthermore, we also show that co-occurrence and traditional (Markov random field) spatial contextual information can be conveniently integrated for further improved patch classification.},
	isbn = {16875176 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-61449241990\&partnerID=40\&md5=b909ab8585061d7873ec0fe14a47dba8},
	author = {Monay, F. and Quelhas, P. and Odobez, J.-M. and Gatica-Perez, D.}
}
