Title
Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search.
Abstract
Binary code learning, a.k.a. hashing, has been successfully applied to the approximate nearest neighbor search in largescale image collections. The key challenge lies in reducing the quantization error from the original real-valued feature space to a discrete Hamming space. Recent advances in unsupervised hashing advocate the preservation of ranking information, which is achieved by constraining the binary code learning to be correlated with pairwise similarity. However, few unsupervised methods consider the preservation of ordinal relations in the learning process, which serves as a more basic cue to learn optimal binary codes. In this paper, we propose a novel hashing scheme, termed Ordinal Constraint Hashing (OCH), which embeds the ordinal relation among data points to preserve ranking into binary codes. The core idea is to construct an ordinal graph via tensor product, and then train the hash function over this graph to preserve the permutation relations among data points in the Hamming space. Subsequently, an in-depth acceleration scheme, termed Ordinal Constraint Projection (OCP), is introduced, which approximates the n-pair ordinal graph by L-pair anchor-based ordinal graph, and reduce the corresponding complexity from Finally, to make the optimization tractable, we further relax the discrete constrains and design a customized stochastic gradient decent algorithm on the Stiefel manifold. Experimental results on serval large-scale benchmarks demonstrate that the proposed OCH method can achieve superior performance over the state-of-the-art approaches.
Year
DOI
Venue
2019
10.1109/TPAMI.2018.2819978
IEEE transactions on pattern analysis and machine intelligence
Keywords
Field
DocType
Binary codes,Tensile stress,Optimization,Measurement,Quantization (signal),Manifolds,Encoding
Pattern recognition,Ranking,Ordinal number,Computer science,Permutation,Binary code,Stiefel manifold,Algorithm,Hash function,Artificial intelligence,Hamming space,Nearest neighbor search
Journal
Volume
Issue
ISSN
41
4
1939-3539
Citations 
PageRank 
References 
9
0.45
19
Authors
4
Name
Order
Citations
PageRank
Hong Liu1779.88
Rongrong Ji23616189.98
Jingdong Wang34198156.76
Chunhua Shen44817234.19