Conference proceeding
Asymmetric Discrete Graph Hashing
THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, v 31(1), pp 2541-2547
13 Feb 2017
Abstract
Recently, many graph based hashing methods have been emerged to tackle large-scale problems. However, there exists two major bottlenecks: (1) directly learning discrete hashing codes is an NP-hard optimization problem; (2) the complexity of both storage and computational time to build a graph with n data points is O(n(2)). To address these two problems, in this paper, we propose a novel yet simple supervised graph based hashing method, asymmetric discrete graph hashing, by preserving the asymmetric discrete constraint and building an asymmetric affinity matrix to learn compact binary codes. Specifically, we utilize two different instead of identical discrete matrices to better preserve the similarity of the graph with short binary codes.We generate the asymmetric affinity matrix using m (m << n) selected anchors to approximate the similarity among all training data so that computational time and storage requirement can be significantly improved. In addition, the proposed method jointly learns discrete binary codes and a low-dimensional projection matrix to further improve the retrieval accuracy. Extensive experiments on three benchmark large-scale databases demonstrate its superior performance over the recent state of the arts with lower training time costs.
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Details
- Title
- Asymmetric Discrete Graph Hashing
- Creators
- Xiaoshuang Shi - University of FloridaFuyong Xing - University of FloridaKaidi Xu - University of FloridaManish Sapkota - University of FloridaLin Yang - University of FloridaAAAI
- Publication Details
- THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, v 31(1), pp 2541-2547
- Series
- AAAI Conference on Artificial Intelligence
- Publisher
- Assoc Advancement Artificial Intelligence
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000485630702083
- Other Identifier
- 991021871487204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic