Subcategory-Aware Object Classification

In this paper, we introduce a subcategory-aware object classification framework to boost category level object classification performance. Motivated by the observation of considerable intra-class diversities and inter-class ambiguities in many current object classification datasets, we explicitly sp...

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Bibliographic Details
Published in2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 827 - 834
Main Authors Jian Dong, Wei Xia, Qiang Chen, Jianshi Feng, Zhongyang Huang, Shuicheng Yan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Ambiguity Modeling
Classification
Data mining
Detectors
Feature extraction
Kernel
Shape
Subcategory Mining
Training
Visualization
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ISSN1063-6919
1063-6919
DOI10.1109/CVPR.2013.112

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Summary:In this paper, we introduce a subcategory-aware object classification framework to boost category level object classification performance. Motivated by the observation of considerable intra-class diversities and inter-class ambiguities in many current object classification datasets, we explicitly split data into subcategories by ambiguity guided subcategory mining. We then train an individual model for each subcategory rather than attempt to represent an object category with a monolithic model. More specifically, we build the instance affinity graph by combining both intra-class similarity and inter-class ambiguity. Visual subcategories, which correspond to the dense sub graphs, are detected by the graph shift algorithm and seamlessly integrated into the state-of-the-art detection assisted classification framework. Finally the responses from subcategory models are aggregated by subcategory-aware kernel regression. The extensive experiments over the PASCAL VOC 2007 and PASCAL VOC 2010 databases show the state-of-the-art performance from our framework.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.112