Hierarchical object categorization with automatic feature selection
Proceedings of the International Multiconference on Computer Science and Information Technology, IMCSIT 2010, vol.5, pp.45-51, 2010 (Scopus)
- Publication Type: Article / Abstract
- Volume: 5
- Publication Date: 2010
- Doi Number: 10.1109/imcsit.2010.5679945
- Journal Name: Proceedings of the International Multiconference on Computer Science and Information Technology, IMCSIT 2010
- Journal Indexes: Scopus
- Page Numbers: pp.45-51
- TED University Affiliated: No
Abstract
In this paper, we have introduced a hierarchical object categorization method with automatic feature selection. A hierarchy obtained by natural similarities and properties is learnt by automatically selected features at different levels. The categorization is a top-down process yielding multiple labels for a test object. We have tested out method and compared the experimental results with that of a nonhierarchical method. It is found that the hierarchical method improves recognition performance at the level of basic classes and reduces error at a higher level. This makes the proposed method plausible for different applications of computer vision including object categorization, semantic image retrieval, and automatic image annotation. © 2010 IEEE.