Promoting Binary Hash Code's Discriminability by Category Centers for Image Retrieval

Abstract

In the field of fast and large-scale image retrieval, the binary hash code based on convolutional features enjoys powerful advantages. CNN model's leading recognition ability shows that its high-level features distribute intensively and have stable between-class distances. On the basis of the distribution characteristic of convolutional features, this paper presents a new method of improving retrieval performance by means of using the category centers of high-level features to enhance the common semantic content of high-level features of the query image and making the consequent binary hash code gain better retrieval results. Experimental results indicate that (i) the category centers of CNN feature offer comparable discriminability with the initial model when identifying the semantic information of images; (ii) before calculating binary hash code, the integration of the CNN feature of a query image with category centers weighted by predicted probabilities can boost performance in similar image retrieval. State-of-the-art retrieval performance is obtained on the Cifar-10 dataset in this paper among all hash methods.