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Heterogeneous Translated Hashing: A Scalable Solution Towards Multi-Modal Similarity Search

Heterogeneous Translated Hashing: A Scalable Solution Towards Multi-Modal Similarity Search Heterogeneous Translated Hashing: A Scalable Solution Towards Multi-Modal Similarity Search YING WEI, Hong Kong University of Science and Technology YANGQIU SONG, University of Illinois at Urbana-Champaign YI ZHEN, Georgia Institute of Technology BO LIU and QIANG YANG, Hong Kong University of Science and Technology Multi-modal similarity search has attracted considerable attention to meet the need of information retrieval across different types of media. To enable efficient multi-modal similarity search in large-scale databases recently, researchers start to study multi-modal hashing. Most of the existing methods are applied to search across multi-views among which explicit correspondence is provided. Given a multi-modal similarity search task, we observe that abundant multi-view data can be found on the Web which can serve as an auxiliary bridge. In this paper, we propose a Heterogeneous Translated Hashing (HTH) method with such auxiliary bridge incorporated not only to improve current multi-view search but also to enable similarity search across heterogeneous media which have no direct correspondence. HTH provides more flexible and discriminative ability by embedding heterogeneous media into different Hamming spaces, compared to almost all existing methods that map heterogeneous data in a common Hamming space. We formulate a joint optimization model to learn hash functions http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Heterogeneous Translated Hashing: A Scalable Solution Towards Multi-Modal Similarity Search

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References (48)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/2744204
Publisher site
See Article on Publisher Site

Abstract

Heterogeneous Translated Hashing: A Scalable Solution Towards Multi-Modal Similarity Search YING WEI, Hong Kong University of Science and Technology YANGQIU SONG, University of Illinois at Urbana-Champaign YI ZHEN, Georgia Institute of Technology BO LIU and QIANG YANG, Hong Kong University of Science and Technology Multi-modal similarity search has attracted considerable attention to meet the need of information retrieval across different types of media. To enable efficient multi-modal similarity search in large-scale databases recently, researchers start to study multi-modal hashing. Most of the existing methods are applied to search across multi-views among which explicit correspondence is provided. Given a multi-modal similarity search task, we observe that abundant multi-view data can be found on the Web which can serve as an auxiliary bridge. In this paper, we propose a Heterogeneous Translated Hashing (HTH) method with such auxiliary bridge incorporated not only to improve current multi-view search but also to enable similarity search across heterogeneous media which have no direct correspondence. HTH provides more flexible and discriminative ability by embedding heterogeneous media into different Hamming spaces, compared to almost all existing methods that map heterogeneous data in a common Hamming space. We formulate a joint optimization model to learn hash functions

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Jul 27, 2016

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