Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2013.112
Title: Subcategory-aware object classification
Authors: Dong, J.
Xia, W.
Chen, Q.
Feng, J.
Huang, Z.
Yan, S. 
Keywords: Ambiguity Modeling
Classification
Subcategory Mining
Issue Date: 2013
Citation: Dong, J., Xia, W., Chen, Q., Feng, J., Huang, Z., Yan, S. (2013). Subcategory-aware object classification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 827-834. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2013.112
Abstract: 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. © 2013 IEEE.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/71894
ISSN: 10636919
DOI: 10.1109/CVPR.2013.112
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