Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval

TL;DR

This paper introduces a deep learning-based hashing method that captures multilevel semantic similarities in multi-label images, improving large-scale image retrieval performance.

Contribution

It proposes a novel deep semantic ranking approach that jointly learns feature representations and hash functions for multi-label images, addressing complex semantic structures.

Findings

Outperforms state-of-the-art hashing methods in ranking metrics

Effectively captures multilevel semantic similarities

Utilizes deep CNNs for feature and hash code learning

Abstract

With the rapid growth of web images, hashing has received increasing interests in large scale image retrieval. Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels. However, most of these hashing methods are designed to handle simple binary similarity. The complex multilevel semantic structure of images associated with multiple labels have not yet been well explored. Here we propose a deep semantic ranking based method for learning hash functions that preserve multilevel semantic similarity between multi-label images. In our approach, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features. Meanwhile, a ranking list that encodes the multilevel similarity information is employed to guide the learning of such deep hash functions. An effective scheme based on surrogate loss is used to solve the intractable optimization problem of nonsmooth and multivariate ranking measures involved in the learning procedure. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods in term of ranking evaluation metrics when tested on multi-label image datasets.