Combination feature for image retrieval in the distributed datacenter

Since the emergence of cloud datacenters provides an enormous amount of resources easily accessible to people, it is challenging to provide an efficient search framework in such a distributed environment. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These methods are insufficient to meet requirements of content based image retrieval (CBIR) and more powerful search frameworks are needed. In this paper, we present LCFIR, an effective image retrieval framework for fast content location in the distributed situation. It adopts the peer-to-peer paradigm and combines color and edge features. The basic idea is to construct multiple replicas of an image's index through exploiting the property of Locality Sensitive Hashing (LSH). Thus, the indexes of similar images are probabilistically gathered into the same node without the knowledge of any global information. The empirical results show that the system is able to yield high accuracy with load balancing, and only contacts a few number of the participating nodes.