The contour-guided color palette (CCP) 1 is proposed
\nfor robust image segmentation. It efficiently integrates con-
\ntour and color cues of an image. To find representative
\ncolors of an image, color samples along long contours be-
\ntween regions, similar in spirit to machine learning method-
\nology that focus on samples near decision boundaries, are
\ncollected followed by the mean-shift (MS) algorithm in the
\nsampled color space to achieve an image-dependent color
\npalette. This color palette provides a preliminary segmen-
\ntation in the spatial domain, which is further fine-tuned by
\npost-processing techniques such as leakage avoidance, fake
\nboundary removal, and small region mergence. Segmenta-
\ntion performances of CCP and MS are compared and an-
\nalyzed. While CCP offers an acceptable standalone seg-
\nmentation result, it can be further integrated into the frame-
\nwork of layered spectral segmentation to produce a more
\nrobust segmentation. The superior performance of CCP-
\nbased segmentation algorithm is demonstrated by experi-
\nments on the Berkeley Segmentation Dataset.<\/p>\n","protected":false},"excerpt":{"rendered":"
The contour-guided color palette (CCP) 1 is proposed for robust image segmentation. It efficiently integrates con- tour and color cues of an image. To find representative colors of an image, color samples along long contours be- tween regions, similar in spirit to machine learning method- ology that focus on samples near decision boundaries, are collected […]<\/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":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"International Conference on Computer Vision (ICCV)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2015-07-29","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-267861","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2015-07-29","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","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":"267867","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"01-2015-98-iccv-ccp","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/07\/01-2015-98-iccv-ccp.pdf","id":267867,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Xiang Fu","user_id":0,"rest_url":false},{"type":"text","value":"Chien-Yi Wang","user_id":0,"rest_url":false},{"type":"text","value":"Chen Chen","user_id":0,"rest_url":false},{"type":"user_nicename","value":"chw","user_id":31440,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=chw"},{"type":"text","value":"C.-C. 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