Relevance feedback based on parameter estimation of target distribution

Author

Sia, K.C. ; King, Irwin

Author_Institution

Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, China

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1974

Lastpage

1979

Abstract

Relevance feedback formulations have been proposed to refine query result in content-based image retrieval in the past few years. Many of them focus on a learning approach to solve the feedback problem. In this paper, we present an expectation maximization approach to estimate the user´s target distribution through user´s feedback. Furthermore, we describe how to use the maximum entropy display to fully utilize user´s feedback information. We detail the process and also demonstrate the result through experiments

Keywords

Gaussian distribution; content-based retrieval; image retrieval; learning (artificial intelligence); maximum entropy methods; parameter estimation; relevance feedback; user interfaces; visual databases; EM algorithm; content-based image retrieval systems; expectation maximization algorithm; image database; learning; maximum entropy display; mixture of Gaussians; parameter estimation; probability; query; relevance feedback; target distribution; user feedback; user target distribution; Computer science; Content based retrieval; Displays; Entropy; Feedback; Image databases; Image retrieval; Image storage; Parameter estimation; Shape;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007822

Filename

1007822