Incorporating prior knowledge into SVM for image retrieval

Author

Wang, Lei ; Xue, Ping ; Chan, Kap Luk

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

2

fYear

2004

fDate

23-26 Aug. 2004

Firstpage

981

Abstract

SVM based image retrieval suffers from the scarcity of labelled samples. In this paper, this problem is solved by incorporating prior knowledge into SVM. Firstly, some prior knowledge of image retrieval is discussed and constructed. After that, the knowledge is incorporated into SVM optimization as a constraint, and a new knowledge-based target function is formulated. Based on this, a framework of image retrieval with knowledge based SVM is proposed. Experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of SVM, especially when the number of labelled samples is small.

Keywords

image retrieval; knowledge based systems; learning (artificial intelligence); optimisation; support vector machines; SVM; image retrieval; knowledge-based target function; learning performance; support vector machine; Constraint optimization; Content based retrieval; Error correction; Feedback; Image databases; Image retrieval; Machine learning; Support vector machine classification; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334423

Filename

1334423