A Fast Retrieval Method Based on Frequent Items Voting of Multi Table and Bucket Map Chain

To solve the problem of strong randomicity and high memory cost of fast retrieval method Locality Sensitive Hashing (LSH) based on random projection, a fast retrieval method is presented based on multi table frequent items voting and bucket map chain on the basis of Exact Euclidean Locality Sensitive Hashing (E2LSH). The method constructs an index matrix with retrieval vectors, and performs frequent items voting and calibration on this matrix to decrease the randomocity. It also reduces the number of points loaded into memory by making use of the data partition property of E2LSH to decrease the memory cost. The experiments show that this method can decrease the randomicity and efficiently reduce the memory cost of retrieval. This is very important for increasing the feasibility of large scale information retrieval especially image retrieval.