Web Image Clustering Based on Multi-instance

Author

Lu, Jing ; Ma, Shaoping ; Zhang, Min

Author_Institution

Dept. of Comput. Sci. & Tech., State Key Lab. of Intell. Tech. & Syst., Beijing

Volume

3

fYear

2008

fDate

9-12 Dec. 2008

Firstpage

467

Lastpage

470

Abstract

In image retrieval and annotation, multi-instance learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi-instance objects based on expectation maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users´ interests on specific Web images without any manual labeled data. And comparative studies have shown the effectiveness of the proposed algorithms.

Keywords

expectation-maximisation algorithm; image retrieval; learning (artificial intelligence); pattern clustering; Web image clustering; expectation maximization; image annotation; image retrieval; iterative heuristic optimization; multiinstance learning; multiinstance objects; Clustering algorithms; Computer science; Content based retrieval; Image retrieval; Information science; Intelligent agent; Intelligent systems; Iterative algorithms; Partitioning algorithms; Supervised learning; Expectation Maximization; Multi-Instance Learning; clustering; iterative heuristic optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on

Conference_Location

Sydney, NSW

Print_ISBN

978-0-7695-3496-1

Type

conf

DOI

10.1109/WIIAT.2008.90

Filename

4740823