The objective of this paper is to examine statistical approaches to the classification of textured
\nmaterials from a single image obtained under unknown viewpoint and illumination.
\nThe approaches investigated here are based on the joint probability distribution of filter
\nresponses.
\nWe review previous work based on this formulation and make two observations. First,
\nwe show that there is a correspondence between the two common representations of filter
\noutputs – textons and binned histograms. Second, we show that two classification methodologies,
\nnearest neighbour matching and Bayesian classification, are equivalent for particular
\nchoices of the distance measure. We describe the pros and cons of these alternative
\nrepresentations and distance measures, and illustrate the discussion by classifying all the
\nmaterials in the Columbia-Utrecht (CUReT) texture database.
\nThese equivalences allow us to perform direct comparisons between the texton frequency
\nmatching framework, best exemplified by the classifiers of Leung and Malik [IJCV 2001],
\nCula and Dana [CVPR 2001], and Varma and Zisserman [ECCV 2002], and the Bayesian
\nframework most closely represented by the work of Konishi and Yuille [CVPR 2000].<\/p>\n","protected":false},"excerpt":{"rendered":"
The objective of this paper is to examine statistical approaches to the classification of textured materials from a single image obtained under unknown viewpoint and illumination. The approaches investigated here are based on the joint probability distribution of filter responses. We review previous work based on this formulation and make two observations. First, we show […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-148212","msr-research-item","type-msr-research-item","status-publish","hentry","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Image and Vision Computing","msr_affiliation":"","msr_published_date":"2004-12-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"1175\u20131183","msr_chapter":"","msr_isbn":"","msr_journal":"Image and Vision Computing","msr_volume":"22","msr_number":"14","msr_editors":"","msr_series":"","msr_issue":"14","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"209737","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"varma04b.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/varma04b.pdf","id":209737,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":209737,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/varma04b.pdf"}],"msr-author-ordering":[{"type":"text","value":"M. 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