Content-Based Image Retrieval using Semantic Assisted Visual Hashing

This is a new technology to support scalable content-based image retrieval (CBIR]), hashing has been recently been focused and future directions of research domain. In this paper, we propose a unique unsupervised visual hashing approach called semantic-assisted visual hashing (SAVH). Distinguished from semi-supervised and supervised visual hashing, its core idea emphatically extracts the rich semantics latently embedded in auxiliary texts of images to boost the effectiveness of visual hashing without any explicit semantic labels. To expand the reach, a unsupervised framework is advanced to learn hash codes by simultaneously preserving visual similarities of images, integrating the semantic assistance from texts on modeling high relationships of inter images and defining the correlations between images and shared contents.