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This work presents an adaptive group testing framework for the range-basedhigh dimensional near neighbor search problem. The proposed method detectshigh-similarity vectors from an extensive collection of high dimensionalvectors, where each vector represents an image descriptor. Our methodefficiently marks each item in the collection as neighbor or non-neighbor onthe basis of a cosine distance threshold without exhaustive search. Like othermethods in the domain of large scale retrieval, our approach exploits theassumption that most of the items in the collection are unrelated to the query.Unlike other methods, it does not assume a large difference between the cosinesimilarity of the query vector with the least related neighbor and that withthe least unrelated non-neighbor. Following the procedure of binary splitting,a multi-stage adaptive group testing algorithm, we split the set of items to besearched into half at each step, and perform dot product tests on smaller andsmaller subsets, many of which we are able to prune away. We experimentallyshow that our method achieves a speed-up over exhaustive search by a factor ofmore than ten with an accuracy same as that of exhaustive search, on a varietyof large datasets. We present a theoretical analysis of the expected number ofdistance computations per query and the probability that a pool with a certainnumber of members will be pruned. In this way, our method exploits very usefuland practical distributional properties unlike other methods. In our method,all required data structures are created purely offline. Moreover, our methoddoes not impose any strong assumptions on the number of true near neighbors, isadaptible to streaming settings where new vectors are dynamically added to thedatabase, and does not require any parameter tuning.

Group Testing for Accurate and Efficient Range-Based Near Neighbor Search : An Adaptive Binary Splitting Approach