Journal
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
Volume -, Issue -, Pages 2633-2641Publisher
IEEE
DOI: 10.1109/CVPR.2017.282
Keywords
-
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available