Long term learning for image retrieval over networks

Author

Picard, David ; Revel, Arnaud ; Cord, Matthieu

Author_Institution

CNRS, Univ. Cergy-Pontoise, Cergy-Pontoise

fYear

2008

fDate

12-15 Oct. 2008

Firstpage

929

Lastpage

932

Abstract

In this paper, we present a long term learning system for content based image retrieval over a network. Relevant feedback is used among different sessions to learn both the similarity function and the best routing for the searched category. Our system is based on mobile agents crawling the network in search of relevant images. An ant-behavior algorithm is used to learn the category dependent routing. With experiments on trecvid´05 key-frame dataset, we show that the smart association of category dependent routing and active learning leads to an improvement of the quality of the retrieval over time.

Keywords

content-based retrieval; human computer interaction; image retrieval; learning (artificial intelligence); mobile agents; relevance feedback; active learning; ant-behavior algorithm; category dependent routing; content based image retrieval; long term learning system; mobile agent; network crawling; relevant feedback; similarity function; user interaction; Computer networks; Concurrent computing; Content based retrieval; Feedback; Image retrieval; Information retrieval; Labeling; Mobile agents; Routing; Software; Cooperative systems; Distributed information systems; Image databases; Information retrieval;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on

Conference_Location

San Diego, CA

ISSN

1522-4880

Print_ISBN

978-1-4244-1765-0

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2008.4711908

Filename

4711908