Fusion feature for LSH-based image retrieval in a cloud 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 LFFIR, a multi-feature image retrieval framework for content similar search in the distributed situation. The key idea is to effectively incorporate image retrieval based on multi-feature into the peer-to-peer (P2P) paradigm. LFFIR fuses the multiple features in order to capture the overall image characteristics. And then it constructs the distributed indexes for the fusion feature through exploiting the property of locality sensitive hashing (LSH). We implement a prototype system to evaluate the system performance with two image datasets. Comprehensive performance evaluations demonstration that our approach brings major performance and accuracy gains compared to the advanced distributed image retrieval framework.

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