Dual Purpose Hashing

TL;DR

This paper introduces Dual Purpose Hashing (DPH), a CNN-based method that efficiently captures both category and attribute similarities for image retrieval, leveraging partially labeled data to outperform specialized methods.

Contribution

The novel DPH method jointly preserves category and attribute similarities using CNNs and handles partially labeled data, enabling efficient dual-purpose image retrieval.

Findings

Achieves comparable or better performance than state-of-the-art methods.

Produces more compact hash codes.

Effectively utilizes partially labeled data.

Abstract

Recent years have seen more and more demand for a unified framework to address multiple realistic image retrieval tasks concerning both category and attributes. Considering the scale of modern datasets, hashing is favorable for its low complexity. However, most existing hashing methods are designed to preserve one single kind of similarity, thus improper for dealing with the different tasks simultaneously. To overcome this limitation, we propose a new hashing method, named Dual Purpose Hashing (DPH), which jointly preserves the category and attribute similarities by exploiting the Convolutional Neural Network (CNN) models to hierarchically capture the correlations between category and attributes. Since images with both category and attribute labels are scarce, our method is designed to take the abundant partially labelled images on the Internet as training inputs. With such a framework, the binary codes of new-coming images can be readily obtained by quantizing the network outputs of a binary-like layer, and the attributes can be recovered from the codes easily. Experiments on two large-scale datasets show that our dual purpose hash codes can achieve comparable or even better performance than those state-of-the-art methods specifically designed for each individual retrieval task, while being more compact than the compared methods.