Web image retrieval reranking with multi-view clustering

ABSTRACT

General image retrieval is often carried out by a text-based search engine, such as Google Image Search. In this case, natural language queries are used as input to the search engine. Usually, the user queries are quite ambiguous and the returned results are not well-organized as the ranking often done by the popularity of an image. In order to address these problems, we propose to use both textual and visual contents of retrieved images to reRank web retrieved results. In particular, a machine learning technique, a multi-view clustering algorithm is proposed to reorganize the original results provided by the text-based search engine. Preliminary results validate the effectiveness of the proposed framework.

REFERENCES

• 1: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 2: Steffen Bickel , Tobias Scheffer, Multi-View Clustering, Proceedings of the Fourth IEEE International Conference on Data Mining, p.19-26, November 01-04, 2004
• 3: X. S. Zhou and T. S. Huang. Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems, 8(6):536--544, 2003.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Design

Keywords:
multi-view clustering, reranking, web image retrieval