Object segmentation and labeling by learning from examples

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dc.contributor.author Xu, Yaowu
dc.contributor.author Saber, Eli
dc.contributor.author Tekalp, Murat
dc.date.accessioned 2009-04-08T15:19:09Z
dc.date.available 2009-04-08T15:19:09Z
dc.date.issued 2003-06
dc.identifier.issn 1057-7149
dc.identifier.uri http://hdl.handle.net/1850/9014
dc.description Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. en_US
dc.description.abstract We propose a system that employs low-level image segmentation followed by color and two-dimensional (2-D) shape matching to automatically group those low-level segments into objects based on their similarity to a set of example object templates presented by the user. A hierarchical content tree data structure is used for each database image to store matching combinations of low-level regions as objects. The system automatically initializes the content tree with only “elementary nodes” representing homogeneous low-level regions. The “learning” phase refers to labeling of combinations of low-level regions that have resulted in successful color and/or 2-D shape matches with the example template( s). These combinations are labeled as “object nodes” in the hierarchical content tree. Once learning is performed, the speed of second-time retrieval of learned objects in the database increases significantly. The learning step can be performed off-line provided that example objects are given in the form of user interest profiles. Experimental results are presented to demonstrate the effectiveness of the proposed system with hierarchical content tree representation and learning by color and 2-D shape matching on collections of car and face images. en_US
dc.language.iso en_US en_US
dc.publisher IEEE Transactions on image processing en_US
dc.relation RIT Scholars content from RIT Digital Media Library has moved from http://ritdml.rit.edu/handle/1850/9014 to RIT Scholar Works http://scholarworks.rit.edu/article/1035, please update your feeds & links!
dc.relation.ispartofseries Vol. 12 en_US
dc.relation.ispartofseries No. 6 en_US
dc.subject Color matching en_US
dc.subject Learning from examples en_US
dc.subject Object annotation en_US
dc.subject Semantic object segmentation en_US
dc.subject Shape matching en_US
dc.title Object segmentation and labeling by learning from examples en_US
dc.type Article en_US

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