SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval

TL;DR

This paper introduces SSDH, a semi-supervised deep hashing method that effectively combines semantic similarity and data structure preservation for large-scale image retrieval, outperforming existing methods.

Contribution

The paper presents the first deep hashing network that jointly learns hash codes and features in a semi-supervised manner, utilizing an online graph construction for improved neighbor capture.

Findings

Outperforms state-of-the-art hashing methods on five datasets.

Effectively preserves semantic similarity and data structures.

Leverages both labeled and unlabeled data during training.

Abstract

Hashing methods have been widely used for efficient similarity retrieval on large scale image database. Traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted features cannot optimally represent the image content and preserve the semantic similarity. Recently, several deep hashing methods have shown better performance because the deep architectures generate more discriminative feature representations. However, these deep hashing methods are mainly designed for supervised scenarios, which only exploit the semantic similarity information, but ignore the underlying data structures. In this paper, we propose the semi-supervised deep hashing (SSDH) approach, to perform more effective hash function learning by simultaneously preserving semantic similarity and underlying data structures. The main contributions are as follows: (1) We propose a semi-supervised loss to jointly minimize the empirical error on labeled data, as well as the embedding error on both labeled and unlabeled data, which can preserve the semantic similarity and capture the meaningful neighbors on the underlying data structures for effective hashing. (2) A semi-supervised deep hashing network is designed to extensively exploit both labeled and unlabeled data, in which we propose an online graph construction method to benefit from the evolving deep features during training to better capture semantic neighbors. To the best of our knowledge, the proposed deep network is the first deep hashing method that can perform hash code learning and feature learning simultaneously in a semi-supervised fashion. Experimental results on 5 widely-used datasets show that our proposed approach outperforms the state-of-the-art hashing methods.