Deep Supervised Hashing for Fast Image Retrieval

TLDR

Image retrieval is challenged by complex appearance variations, but recent advances in CNNs have improved robust representation learning. This work proposes Deep Supervised Hashing (DSH) to learn compact, similarity‑preserving binary codes for efficient large‑scale image retrieval. DSH trains a CNN on pairs of similar/dissimilar images, using a loss that maximizes output discriminability while regularizing real‑valued activations toward discrete +1/−1 values, and then quantizes the network outputs to binary codes for queries. Experiments on CIFAR‑10 and NUS‑WIDE demonstrate that DSH outperforms state‑of‑the‑art hashing methods.

Abstract

In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets. While the complex image appearance variations still pose a great challenge to reliable retrieval, in light of the recent progress of Convolutional Neural Networks (CNNs) in learning robust image representation on various vision tasks, this paper proposes a novel Deep Supervised Hashing (DSH) method to learn compact similarity-preserving binary code for the huge body of image data. Specifically, we devise a CNN architecture that takes pairs of images (similar/dissimilar) as training inputs and encourages the output of each image to approximate discrete values (e.g. +1/-1). To this end, a loss function is elaborately designed to maximize the discriminability of the output space by encoding the supervised information from the input image pairs, and simultaneously imposing regularization on the real-valued outputs to approximate the desired discrete values. For image retrieval, new-coming query images can be easily encoded by propagating through the network and then quantizing the network outputs to binary codes representation. Extensive experiments on two large scale datasets CIFAR-10 and NUS-WIDE show the promising performance of our method compared with the state-of-the-arts.