Citation

Pang,, Junbiao and Cheng,, Jian and Liu,, Jing and Zhang,, Chunjie and Liang,, Chao and Tian, Qi and Huang,, Qingming (2014) Object categorization in sub-semantic space. Neurocomputing.

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Abstract

Due to the semantic gap, the low-level features are unsatisfactory for object categorization. Besides, the use of semantic related image representation may not be able to cope with large inter class variations and are not very robust to noise. To solve these problems, in this paper, we propose a novel object categorization method by using the sub-semantic space based image representation. First, examplar classifiers are trained by separating each training image from the others and serve as the weak semantic similarity measurement. Then a graph is constructed by combining the visual similarity and weak semantic similarity of these training images. We partition this graph into visually and semantically similar sub-sets. Each sub-set of images are then used to train classifiers in order to separate this sub-set from the others. The learned sub-set classifiers are then used to construct a sub-semantic space based representation of images. This sub-semantic space is not only more semantically meaningful than examplar based representation but also more reliable and resistant to noise than traditional semantic space based image representation. Finally, we make categorization of objects using this sub-semantic space with structure regularized SVM classifier and conduct experiments on several public datasets to demonstrate the effectiveness of the proposed method.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Jun 2014 09:34
Last Modified:	04 Jun 2014 09:34
URII:	http://shdl.mmu.edu.my/id/eprint/5557

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