Unsupervised Hashing with Similarity Distribution Calibration

TL;DR

This paper introduces a novel unsupervised hashing method called Similarity Distribution Calibration (SDC) that aligns hash code similarity distributions to prevent collapse and improve retrieval performance.

Contribution

The paper proposes SDC, a new calibration approach that preserves similarity distribution in hash codes, addressing the similarity collapse issue in unsupervised hashing.

Findings

SDC significantly outperforms state-of-the-art methods in image retrieval.

SDC effectively alleviates the similarity collapse problem.

Experiments demonstrate improved similarity preservation across datasets.

Abstract

Unsupervised hashing methods typically aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes. However, these methods often overlook the fact that the similarity between data points in the continuous feature space may not be preserved in the discrete hash code space, due to the limited similarity range of hash codes. The similarity range is bounded by the code length and can lead to a problem known as similarity collapse. That is, the positive and negative pairs of data points become less distinguishable from each other in the hash space. To alleviate this problem, in this paper a novel Similarity Distribution Calibration (SDC) method is introduced. SDC aligns the hash code similarity distribution towards a calibration distribution (e.g., beta distribution) with sufficient spread across the entire similarity range, thus alleviating the similarity collapse problem. Extensive experiments show that our SDC outperforms significantly the state-of-the-art alternatives on coarse category-level and instance-level image retrieval. Code is available at https://github.com/kamwoh/sdc.