Procrustean Orthogonal Sparse Hashing

TL;DR

This paper introduces Procrustean Orthogonal Sparse Hashing (POSH), a biologically inspired, orthogonality-enhanced hashing method that improves similarity search accuracy, supported by theoretical analysis and extensive empirical validation.

Contribution

It proposes a novel orthogonal sparse hashing method, POSH, unifying biological insights with optimization, and addresses limitations of previous olfaction-inspired hashing techniques.

Findings

POSH outperforms existing hashing methods on standard benchmarks.

Orthogonality enhances the accuracy of sparse hashing.

Theoretical analysis reveals shortcomings of prior olfaction-inspired methods.

Abstract

Hashing is one of the most popular methods for similarity search because of its speed and efficiency. Dense binary hashing is prevalent in the literature. Recently, insect olfaction was shown to be structurally and functionally analogous to sparse hashing [6]. Here, we prove that this biological mechanism is the solution to a well-posed optimization problem. Furthermore, we show that orthogonality increases the accuracy of sparse hashing. Next, we present a novel method, Procrustean Orthogonal Sparse Hashing (POSH), that unifies these findings, learning an orthogonal transform from training data compatible with the sparse hashing mechanism. We provide theoretical evidence of the shortcomings of Optimal Sparse Lifting (OSL) [22] and BioHash [30], two related olfaction-inspired methods, and propose two new methods, Binary OSL and SphericalHash, to address these deficiencies. We compare POSH, Binary OSL, and SphericalHash to several state-of-the-art hashing methods and provide empirical results for the superiority of the proposed methods across a wide range of standard benchmarks and parameter settings.