Web Image Clustering Based on Multi-instance

Authors:
Jing Lu;ShaoPing Ma;Min Zhang
Affiliations:
-;-;-
Venue:
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Year:
2008

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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.