CBIR method based on improved PQ algorithm
An algorithm and product technology, applied in the CBIR field based on the improved PQ algorithm, can solve the problems of high operation and maintenance cost and time-consuming, and achieve the effect of improving the recall rate, good nonlinear retrieval, and optimizing the time-consuming algorithm.
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[0077] 1. Conduct index algorithm test:
[0078] Using SIFT1M as the test data set, the improved and optimized IVPQ encoding retrieval algorithm in this paper is compared with several existing image retrieval algorithms based on ANN retrieval strategies, respectively from the three indicators of recall rate, retrieval time, and index file size. To measure the superiority of the retrieval algorithm. They are the unimproved product quantization method PQ proposed in the literature, the local sensitive hash method MLSH for multi-table query proposed in the literature, and the index quantization method HNSW proposed in the literature. The experimental parameters are described as follows: nlist: indicates the number of sample clusters; m: indicates the number of divided subspaces; nbit: indicates the number of binary coded bits of each vector subspace; nprobe indicates the number of the most similar class when querying number; R@n indicates the recall rate of returning the n most ...
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