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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tao, Liang (陶亮) | |
dc.contributor.author | Ip, Horace Ho Shing | |
dc.date.accessioned | 2013-01-29T03:58:20Z | |
dc.date.accessioned | 2017-09-19T09:19:07Z | |
dc.date.accessioned | 2019-02-12T08:40:50Z | - |
dc.date.available | 2013-01-29T03:58:20Z | |
dc.date.available | 2017-09-19T09:19:07Z | |
dc.date.available | 2019-02-12T08:40:50Z | - |
dc.date.issued | 2012-12 | |
dc.identifier.other | cs2012-002 | |
dc.identifier.uri | http://144.214.8.231/handle/2031/6806 | - |
dc.description.abstract | Approximate nearest neighbor search within large scale image datasets strongly demands efficient and effective algorithms. One promising strategy is to compute compact bits string via the hashing scheme as representation of data examples, which can dramatically reduce query time and storage requirements. In this paper, we propose a novel Cauchy graph-based hashing algorithm for the first time, which can capture more local topology semantics than Laplacian embedding. In particular, greater similarities are achieved through Cauchy embedding mapped from the pairs of smaller distance over the original data space. Then regularized kernel least-squares, with its closed form solution, is applied to efficiently learn hash functions. The experimental evaluations over several noted image retrieval benchmarks, MNIST, CIFAR-10 and USPS, demonstrate that performance of the proposed hashing algorithm is quite comparable with the state-of-the-art hashing techniques in searching semantic similar neighbors, especially in quite short length hash codes, such as those of only 4, 6, and 8 bits. | |
dc.rights | This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner. | |
dc.rights | Access is restricted to CityU and other institutions for the purpose of scholarly communication. | |
dc.subject | Image Retrieval | |
dc.subject | Hashing and Machine Learning | |
dc.title | Hashing with Cauchy Graph | en_US |
dc.type | Article | en_US |
dc.contributor.department | Department of Computer Science | en_US |
dc.description.award | Won the Best Paper Award (Machine Learning) at the 2012 Pacific-Rim Conference on Multimedia (PCM 2012) organized by Nanyang Technological University, Singapore and National University of Singapore. | |
dc.description.fulltext | Award winning work is available. | |
dc.description.supervisor | Prof. Ip, Horace Ho-shing | en_US |
Appears in Collections: | Student Works With External Awards |
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award_news.html | 148 B | HTML | View/Open | |
award_winning_work.html | 163 B | HTML | View/Open |
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