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We present a cooperative framework for content-based image retrieval for the realistic setting where images are distributed across multiple cooperating servers. The proposed method is in line with bag-of-features approaches but uses fully data-independent, randomized structures, shared by the cooperating servers, to map image features to common visual words. A coherent, global image similarity measure (which is a kernel) is computed in a distributed fashion over visual words, by only requiring a small amount of data transfers between nodes. Our experiments on various image types show that this framework is a very promising step towards large-scale, distributed content-based image retrieval.

Incremental indexing and distributed image search using shared randomized vocabularies