Multiple features for image retrieval in distributed datacenter

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose the scalable image retrieval framework which can efficiently support content similarity search in the distributed environment. Its key idea is to integrate image fusion features into distributed hash tables (DHTs) by exploiting the property of the locality sensitive hashing (LSH). Thus, the images with similar content are most likely gathered into the same node without the knowledge of any global information. To the best of our knowledge, there is less comprehensive study on large-scale CBIR with fusion features in the distributed environment.