Distributed Architecture for Large Scale Image-Based Search

In recent years, some computer vision algorithms such as SIFT (scale invariant feature transform) have been employed in image similarity match to perform image-based search applications. However, with the increasing scale of image databases, centralized image retrieval system no longer provide adequate prompt search. In this paper, we design a scalable distributed architecture, which is analog to Web search engine, for efficient large-scale image retrieval. In our distributed architecture, images are partitioned to multiple servers and an index is built. Administrated by a controlling server, each distributed server matches query image in its own image sub-collection in parallel and returns the intermediate search results, a list of images similar to query image, to the controlling server for further re-ranking and merging. An evaluation of the results shows that our distributed architecture removes the limitation of a centralized image retrieval system. By performing reasonable indexing, merging and ranking strategies, the precision level of search is near to that performed on standalone retrieval systems indexing all images.