Cited By
View all- Jin LLi KHu HQi GTang J(2018)Semantic Neighbor Graph Hashing for Multimodal RetrievalIEEE Transactions on Image Processing10.1109/TIP.2017.277674527:3(1405-1417)Online publication date: Mar-2018
SimH: A Supervised Cross-View Hashing Framework Preserving Semantic Similarities in Hamming Space
Pages 217 - 222
Abstract
To tackle the scalability issues for cross-view retrieval on large-scale databases, in this paper we propose a supervised cross-view hashing framework termed SimH that can well preserve semantic similarities of objects in Hamming space. The proposed SimH generates one unified hash code for all views of an object. For off-line training, SimH firstly exploits the similarity matrix of training objects to learn their corresponding similarity preserving hash codes and then learns hash functions for each view to map features into hash codes, which can be open for any predictive model. Afterwards, the hash codes learnt during training are discarded. For online hash encoding, given an unseen object, learnt hash functions in each of its observed views will firstly predict view-specific hashing results and then a novel expected value based combining strategy is utilized to merge them and determine the unified hash code. Experiments on benchmark datasets show that SimH outperforms several state-of-the-art cross-view hashing methods.
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- SimH: A Supervised Cross-View Hashing Framework Preserving Semantic Similarities in Hamming Space
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Published In
August 2016
360 pages
ISBN:9781450348508
DOI:10.1145/3007669
Copyright © 2016 ACM.
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Published: 19 August 2016
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ICIMCS'16
ICIMCS'16: International Conference on Internet Multimedia Computing and Service
August 19 - 21, 2016
Xi'an, China
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ICIMCS'16 Paper Acceptance Rate 77 of 118 submissions, 65%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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View all- Jin LLi KHu HQi GTang J(2018)Semantic Neighbor Graph Hashing for Multimodal RetrievalIEEE Transactions on Image Processing10.1109/TIP.2017.277674527:3(1405-1417)Online publication date: Mar-2018
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