Sequential and Correlated Image Hash Code Generation with Deep Reinforcement Learning

Abstract

Image hashing is an algorithm used to represent an image with a unique value. Hashing methods, which are generally developed to search for similar examples of an image, have gained a new dimension with the use of deep network structures and better results have started to be obtained with the methods. The developed deep network models generally consider hash functions independently and do not take into account the correlation between them. In addition, most of the existing data-dependent hashing methods use pairwise/triplet similarity metrics that capture data relationships from a local perspective. In this study, the Central similarity metric, which can achieve better results, is adapted to the deep reinforcement learning method with sequential learning strategy, and successful results are obtained in learning binary hash codes. By taking into account the errors of previous hash functions in the deep reinforcement learning strategy, a new model is presented that performs interrelated and central similarity based learning.