Query specific re-ranking for improved cross-modal retrieval

Mandal, Devraj and Biswas, Soma (2017) Query specific re-ranking for improved cross-modal retrieval. In: PATTERN RECOGNITION LETTERS, 98 . pp. 110-116.

PDF PAT_REC_LET_98_110_2017.pdf - Published Version Restricted to Registered users only Download (2MB) | Request a copy

Abstract

Cross-modal retrieval tasks like image-to-text, audio-to-image retrieval, etc. are an important area of research. Different algorithms have been developed to address these tasks. In this work, we propose a novel query specific re-ranking based approach to improve the retrieval performance of any given baseline approach. For each query, the top K-retrieved results of the baseline algorithm are used to compute its class-rank order feature. Based on this feature of the query and the highly relevant examples within the top K-retrieved results, each training example is given a score indicating its relevance to the query, which is finally used to train the query-specific regressor. The new score given by this regressor to each retrieved example is then used to re-rank them. The proposed approach does not require knowledge of the baseline algorithm, and also does not extract additional features from the data. Thus it can be used as an add-on to any existing algorithm for improved retrieval performance. Experiments with several state-of-the-art cross-modal algorithms across different datasets show the effectiveness of the proposed re-ranking algorithm. (C) 2017ElsevierB. V. Allrightsreserved.

Item Type:	Journal Article
Publication:	PATTERN RECOGNITION LETTERS
Additional Information:	Copy right for this article belongs to the ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	21 Oct 2017 06:18
Last Modified:	21 Oct 2017 06:18
URI:	http://eprints.iisc.ac.in/id/eprint/58047

Actions (login required)

View Item