Image Hashing by Minimizing Independent Relaxed Wasserstein Distance

Abstract

Image hashing is a fundamental problem in the computer vision domain with various challenges, primarily, in terms of efficiency and effectiveness. Existing hashing methods lack a principled characterization of the goodness of the hash codes and a principled approach to learn the discrete hash functions that are being optimized in the continuous space. Adversarial autoencoders are shown to be able to implicitly learn a robust hash function that generates hash codes which are balanced and have low-quantization error. However, the existing adversarial autoencoders for hashing are too inefficient to be employed for large-scale image retrieval applications because of the minmax optimization procedure. In this paper, we propose an Independent Relaxed Wasserstein Autoencoder, which presents a novel, efficient hashing method that can implicitly learn the optimal hash function by directly training the adversarial autoencoder without any discriminator/critic. Our method is an order-of-magnitude more efficient and has a much lower sample complexity than the Optimal Transport formulation of the Wasserstein distance. The proposed method outperforms the current state-of-the-art image hashing methods for the retrieval task on several prominent image collections.

Saurav Manchanda
Saurav Manchanda
Ph.D. Candidate, Computer Science

My research interests include Graph Neural Networks, Information Retrieval and Computation Linguistics.

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