tailieunhanh - Báo cáo hóa học: "Research Article Contextual Classification of Image Patches with Latent Aspect Models"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Contextual Classification of Image Patches with Latent Aspect Models | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2009 Article ID 602920 20 pages doi 2009 602920 Research Article Contextual Classification of Image Patches with Latent Aspect Models Florent Monay 1 Pedro Quelhas 2 Jean-Marc Odobez 1 3 and Daniel Gatica-Perez1 3 1Idiap Research Institute Martigny 1920 Martigny Switzerland 2Instituto de Engenharia Biomedica INEB Campus da FEUP 4200-465 Porto Portugal 3 Swiss Federal Institute of Technology EPFL 1015 Lausanne Switzerland Correspondence should be addressed to Florent Monay Received 21 May 2008 Accepted 24 October 2008 Recommended by Simon Lucey 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-specific what 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. Copyright 2009 Florent Monay et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction Associating semantic .