Context based object categorization: A critical survey
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Context based object categorization : A critical survey. / Galleguillos, Carolina; Belongie, Serge.
In: Computer Vision and Image Understanding, Vol. 114, No. 6, 06.2010, p. 712-722.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Context based object categorization
T2 - A critical survey
AU - Galleguillos, Carolina
AU - Belongie, Serge
PY - 2010/6
Y1 - 2010/6
N2 - 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.
AB - 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.
KW - Computer vision systems
KW - Context
KW - Object categorization
KW - Object recognition
UR - http://www.scopus.com/inward/record.url?scp=78651403274&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2010.02.004
DO - 10.1016/j.cviu.2010.02.004
M3 - Journal article
AN - SCOPUS:78651403274
VL - 114
SP - 712
EP - 722
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
IS - 6
ER -
ID: 302047761