Histograms of visual words (or textons) have proved e\ufb00ective in tasks such as image classi\ufb01cation and object class recognition. A common approach is to represent an object class by a set of histograms, each one corresponding to a training exemplar. Classi\ufb01cation is then achieved by k-nearest neighbour search over the exemplars.<\/p>\n
In this paper we introduce two novelties on this approach: (i) we show that new compact single histogram models estimated optimally from the entire training set achieve an equal or superior classi\ufb01cation accuracy. The bene\ufb01t of the single histograms is that they are much more e\ufb03cient both in terms of memory and computational resources; and (ii) we show that bag of visual words histograms can provide an accurate pixel-wise segmentation of an image into object class regions. In this manner the compact models of visual object classes give simultaneous segmentation and recognition of image regions.<\/p>\n
The approach is evaluated on the MSRC database [5] and it is shown that performance equals or is superior to previous publications on this database.<\/p>\n","protected":false},"excerpt":{"rendered":"
Histograms of visual words (or textons) have proved e\ufb00ective in tasks such as image classi\ufb01cation and object class recognition. A common approach is to represent an object class by a set of histograms, each one corresponding to a training exemplar. Classi\ufb01cation is then achieved by k-nearest neighbour search over the exemplars. In this paper we […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-153615","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proc. 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