ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Generalized Semantic Preserving Hashing for n-Label Cross-Modal Retrieval

Mandal, Devraj and Chaudhury, Kunal N and Biswas, Soma (2017) Generalized Semantic Preserving Hashing for n-Label Cross-Modal Retrieval. In: 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), JUL 21-26, 2016, Honolulu, HI, pp. 2633-2641.

[img] PDF
Ieee_Con_Com_2633_2017.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: http://dx.doi.org/10.1109/CVPR.2017.282

Abstract

Due to availability of large amounts of multimedia data, cross-modal matching is gaining increasing importance. Hashing based techniques provide an attractive solution to this problem when the data size is large. Different scenarios of cross-modal matching are possible, for example, data from the different modalities can be associated with a single label or multiple labels, and in addition may or may not have one-to-one correspondence. Most of the existing approaches have been developed for the case where there is one-to-one correspondence between the data of the two modalities. In this paper, we propose a simple, yet effective generalized hashing framework which can work for all the different scenarios, while preserving the semantic distance between the data points. The approach first learns the optimum hash codes for the two modalities simultaneously, so as to preserve the semantic similarity between the data points, and then learns the hash functions to map from the features to the hash codes. Extensive experiments on single label dataset like Wiki and multi-label datasets like NUS-WIDE, Pascal and LabelMe under all the different scenarios and comparisons with the state-of-the-art shows the effectiveness of the proposed approach.

Item Type: Conference Paper
Additional Information: 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, JUL 21-26, 2016 Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 20 Jan 2018 05:44
Last Modified: 20 Jan 2018 05:44
URI: http://eprints.iisc.ac.in/id/eprint/58844

Actions (login required)

View Item View Item