Hierarchical deep semantic hashing for fast image retrieval

Content-Based large-scale image retrieval has recently attracted considerable attention because of the explosive increase of online images. Inspired by recent advances in convolutional neural networks, we propose a hierarchical deep semantic method for learning similarity function that solves the problems of precision and speed of retrieval in the setting of large-scale environments. The distinctive contribution of our work is a novel approach that can utilize previous knowledge of the semantic hierarchy. When semantic information and a related hierarchy structure are available, significant improvements can be attained. Exploiting hierarchical relationships is the most important thing for large-scale issues. Binary code can be learned from deep neural network for representing the latent concepts that dominate the semantic labels. Different from other supervised methods that require learning an explicit hashing function to map the binary code features from the images, our method learns Hierarchical Deep Semantic Hashing code (HDSH-code) and image representations in an implicit manner, making it suitable for large-scale datasets. An additional contribution is a novel hashing scheme (generated at the same time with semantic information) that is able to reduce the computational cost of retrieval. Comprehensive experiments were conducted on Holidays, Oxford5k/105k, Caltech256 retrieval datasets, our HDSH performs competitively even when the convolutional neural network has been pre-trained for a surrogate unrelated task. We further demonstrates its efficacy and scalability on a large-scale dataset Imagenet with millions of images. With deep hierarchical semantic hashing, we report retrieval times are 0.15ms and 53.92ms on Holidays and Imagenet dataset, respectively.