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A document expansion framework for tag-based image retrieval

Wei Lu (School of Information Management, Wuhan University, Wuhan, China)
Heng Ding (School of Information Management, Wuhan University, Wuhan, China)
Jiepu Jiang (College of Information and Computer Sciences, University of Massachusetts, Amherst, Massachusetts, USA)

Aslib Journal of Information Management

ISSN: 2050-3806

Article publication date: 15 January 2018

590

Abstract

Purpose

The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR).

Design/methodology/approach

The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate).

Findings

The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|RD) is able to erase the negative influences of error images in RD.

Research limitations/implications

Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach.

Originality/value

Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.

Keywords

Citation

Lu, W., Ding, H. and Jiang, J. (2018), "A document expansion framework for tag-based image retrieval", Aslib Journal of Information Management, Vol. 70 No. 1, pp. 47-65. https://doi.org/10.1108/AJIM-05-2017-0133

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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