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Generalized Semantic Preserving Hashing for Cross-Modal Retrieval

Mandal, Devraj and Chaudhury, Kunal N and Biswas, Soma (2018) Generalized Semantic Preserving Hashing for Cross-Modal Retrieval. In: IEEE TRANSACTIONS ON IMAGE PROCESSING, 28 (1). pp. 102-112.

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Official URL: http://dx.doi.org/10.1109/TIP.2018.2863040


Cross-modal retrieval is gaining importance due to the availability of large amounts of multimedia data. Hashingbased techniques provide an attractive solution to this problem when the data size is large. For cross-modal retrieval, data from the two modalities may be associated with a single label or multiple labels, and in addition, may or may not have a one-to-one correspondence. This work proposes a simple hashing framework which has the capability to work with different scenarios while effectively capturing the semantic relationship between the data items. The work proceeds in two stages in which the first stage learns the optimum hash codes by factorizing an affinity matrix, constructed using the label information. In the second stage, ridge regression and kernel logistic regression is used to learn the hash functions for mapping the input data to the bit domain. We also propose a novel iterative solution for cases where the training data is very large, or when the whole training data is not available at once. Extensive experiments on single label data set like Wiki and multi-label datasets like MirFlickr, NUS-WIDE, Pascal, and LabelMe, and comparisons with the state-of-the-art, shows the usefulness of the proposed approach.

Item Type: Journal Article
Additional Information: Copy right for this article belong to IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords: Cross-modal retrieval; hashing; multi-label data; unpaired matching scenarios; kernel logistic regression
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 09 Oct 2018 15:44
Last Modified: 10 Oct 2018 15:17
URI: http://eprints.iisc.ac.in/id/eprint/60832

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