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Due to its low storage cost and fast query speed, hashing has been widelyused for large-scale approximate nearest neighbor (ANN) search. Bucket search,also called hash lookup, can achieve fast query speed with a sub-linear timecost based on the inverted index table constructed from hash codes. Manymetrics have been adopted to evaluate hashing algorithms. However, all existingmetrics are improper to evaluate the hash codes for bucket search. On one hand,all existing metrics ignore the retrieval time cost which is an importantfactor reflecting the performance of search. On the other hand, some of them,such as mean average precision (MAP), suffer from the uncertainty problem asthe ranked list is based on integer-valued Hamming distance, and areinsensitive to Hamming radius as these metrics only depend on relative Hammingdistance. Other metrics, such as precision at Hamming radius R, fail toevaluate global performance as these metrics only depend on one specificHamming radius. In this paper, we first point out the problems of existingmetrics which have been ignored by the hashing community, and then propose anovel evaluation metric called radius aware mean average precision (RAMAP) toevaluate hash codes for bucket search. Furthermore, two coding strategies arealso proposed to qualitatively show the problems of existing metrics.Experiments demonstrate that our proposed RAMAP can provide more properevaluation than existing metrics.

On the Evaluation Metric for Hashing