Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2013

Abstract

Search reranking is regarded as a common way to boost retrieval precision. The problem nevertheless is not trivial especially when there are multiple features or modalities to be considered for search, which often happens in image and video retrieval. This paper proposes a new reranking algorithm, named circular reranking, that reinforces the mutual exchange of information across multiple modalities for improving search performance, following the philosophy that strong performing modality could learn from weaker ones, while weak modality does benefit from interacting with stronger ones. Technically, circular reranking conducts multiple runs of random walks through exchanging the ranking scores among different features in a cyclic manner. Unlike the existing techniques, the reranking procedure encourages interaction among modalities to seek a consensus that are useful for reranking. In this paper, we study several properties of circular reranking, including how and which order of information propagation should be configured to fully exploit the potential of modalities for reranking. Encouraging results are reported for both image and video retrieval on Microsoft Research Asia Multimedia image dataset and TREC Video Retrieval Evaluation 2007-2008 datasets, respectively.

Keywords

Circular reranking, multimodality fusion, visual search

Discipline

Data Storage Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Image Processing

Volume

22

Issue

4

First Page

1642

Last Page

1653

ISSN

1057-7149

Identifier

10.1109/TIP.2012.2236341

Publisher

Institute of Electrical and Electronics Engineers

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