Semantic Image Retrieval With Feature Space Rankings

Keywords

image retrieval; rank correlation; similarity search; subspace learning; Supervised hashing

Abstract

Learning to hash is receiving increasing research attention due to its effectiveness in addressing the large-scale similarity search problem. Most of the existing hashing algorithms are focused on learning hash functions in the form of numeric quantization of some projected feature space. In this work, we propose a novel hash learning method that encodes features' relative ordering instead of quantizing their numeric values in a set of low-dimensional ranking subspaces. We formulate the ranking-based hash learning problem as the optimization of a continuous probabilistic error function using softmax approximation and present an efficient learning algorithm to solve the problem. As a generalization of Winner-Take-All (WTA) hashing, the proposed algorithm naturally enjoys the numeric stability benefits of rank correlation measures while being optimized to achieve high precision with very compact code. Additionally, the proposed method can also be easily extended to nonlinear kernel spaces to discover ranking structures that can not be revealed in linear subspaces. We demonstrate through extensive experiments that the proposed method can achive competitive performances as compared to a number of state-of-The-Art hashing methods.

Publication Date

6-1-2017

Publication Title

International Journal of Semantic Computing

Volume

11

Issue

2

Number of Pages

171-192

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1142/S1793351X17400074

Socpus ID

85040312269 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85040312269

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