Simultaneous compression and quantization: a joint approach for efficient unsupervised hashing

Tuan Hoang, Thanh-Toan Do, Huu Le, Ngai-Man Cheung

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Abstract

For unsupervised data-dependent hashing, the two most important requirements are to preserve similarity in the low-dimensional feature space and to minimize the binary quantization loss. A well-established hashing approach is Iterative Quantization (ITQ), which addresses these two requirements in separate steps. In this paper, we revisit the ITQ approach and propose novel formulations and algorithms to the problem. Specifically, we propose a novel approach, named Simultaneous Compression and Quantization (SCQ), to jointly learn to compress (reduce dimensionality) and binarize input data in a single formulation under strict orthogonal constraint. With this approach, we introduce a loss function and its relaxed version, termed Orthonormal Encoder (OnE) and Orthogonal Encoder (OgE) respectively, which involve challenging binary and orthogonal constraints. We propose to attack the optimization using novel algorithms based on recent advance in cyclic coordinate descent approach. Comprehensive experiments on unsupervised image retrieval demonstrate that our proposed methods consistently outperform other state-of-the-art hashing methods. Notably, our proposed methods outperform recent deep neural networks and GAN based hashing in accuracy, while being very computationally-efficient.

Original languageEnglish
Article number102852
Number of pages10
JournalComputer Vision and Image Understanding
Volume191
DOIs
Publication statusPublished - Feb 2020
Externally publishedYes

Cite this