Deep Center Supervised Hash for Fast Image Retrieval

Abstract

With the rapid growth of image data, the current mainstream image retrieval method uses the handcraft visual feature coding, which lack of learning ability, resulting in its image expression is not strong, and the large visual feature dimension which seriously restricting its image retrieval performance. The basic idea of this paper is to learn the hash center of each class, so that the distance of intra-class is closer in Hamming space, while maintaining the Hamming distance between the interclass. Firstly, the deep implicit relationship of the training image is extracted by using the robust learning ability of the CNN, and enhance the distinguishing and expressive ability of the image hash feature. In particular, we have designed a CNN architecture in which a hash layer is added to encourage the input image to approximate discrete value (e.g. +1/-1). At the same time, we have carefully designed a hash center loss function, so that the distance between the intra-class to disperse, closer within the intra-class distance, and simultaneously imposing regularization on the real-valued outputs to approximate desired discrete values. We have conducted evolution on CIFAR-10 and NUS-WIDE images benchmarks, demonstrating that our approach can provide superior image search accuracy than other state-of-the-art hashing methods.