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Context based object categorization: A critical survey

Galleguillos, Carolina and Belongie, Serge (2010) Context based object categorization: A critical survey. Computer Vision and Image Understanding, 114 (6). pp. 712-722. ISSN 1077-3142. http://resolver.caltech.edu/CaltechAUTHORS:20100624-082820148

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Abstract

The goal of object categorization is to locate and identify instances of an object category within an image. Recognizing an object in an image is difficult when images include occlusion, poor quality, noise or background clutter, and this task becomes even more challenging when many objects are present in the same scene. Several models for object categorization use appearance and context information from objects to improve recognition accuracy. Appearance information, based on visual cues, can successfully identify object classes up to a certain extent. Context information, based on the interaction among objects in the scene or global scene statistics, can help successfully disambiguate appearance inputs in recognition tasks. In this work we address the problem of incorporating different types of contextual information for robust object categorization in computer vision. We review different ways of using contextual information in the field of object categorization, considering the most common levels of extraction of context and the different levels of contextual interactions. We also examine common machine learning models that integrate context information into object recognition frameworks and discuss scalability, optimizations and possible future approaches.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/j.cviu.2010.02.004DOIUNSPECIFIED
Additional Information:© 2010 Elsevier. Received 30 September 2008; accepted 22 February 2010. Available online 1 March 2010.
Subject Keywords:Object recognition; Context; Object categorization; Computer vision systems
Record Number:CaltechAUTHORS:20100624-082820148
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20100624-082820148
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18786
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:14 Jul 2010 19:19
Last Modified:26 Dec 2012 12:10

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