Region annotations in hashing based image retrieval

During recent years, large-scale data has become more and more present in our daily life, requiring better, faster, and more effective ways to discriminate between unrelated content and what we are truly interested in. In particular, retrieving similar images is a fundamental problem in both image processing and computer vision. Since finding similar images high-dimensional image features is not feasible in practice, approximate hashing techniques have been proved very effective due to their great efficiency and reasonably accuracy. Current hashing methods focus on image as a whole. However, sometimes the relevant content might be just bounded to a region of interest while the background part seems to be needless for users. In this paper, we propose to exploit region-level annotations, whenever they are available, for both training supervised hashing methods and also in the query image. Our objective is to evaluate whether region-level features can be helpful in a supervised hashing scenario. Experimental results confirm that region-level annotations in both the query and training images increase the accuracy.