Large-scale image high-speed retrieval method based on multi-view enhanced depth hash

Abstract

The invention discloses a large-scale image high-speed retrieval method based on multi-view enhanced deep hash. The method comprises the following steps: step 1, acquiring image multi-view feature representation; step 2, calculating a view relation matrix; step 3, designing a loss function of the model; step 4, performing fusion and enhancement; step 5, training the built model on a large-scale image training data set; step 6, testing the trained model to generate a hash code, and then performing hash retrieval; step 7, carrying out an experiment to evaluate the index. According to the method,the influence of Hamming radius extension on the result is small; and along with the increase of the code length, the precision is kept stable.

Description

technical field [0001] The invention belongs to the technical field of computer images and artificial intelligence, and specifically solves the problem of high-speed retrieval of large-scale image data sets. The method involves new theories such as multi-view, deep learning and hash learning. Background technique [0002] With the explosive growth of image data, efficient large-scale image retrieval algorithms are urgently needed for many tasks. Approximate nearest neighbor search, which balances time-consuming and efficient retrieval on large-scale datasets, has attracted increasing attention. Hashing is an efficient method for nearest neighbor search in large-scale data spaces by embedding high-dimensional feature descriptors in similarity-preserving Hamming spaces with low dimensions. However, compared with traditional retrieval methods, large-scale high-speed retrieval through binary codes has a certain degree of reduction in retrieval accuracy. [0003] Hashing learni...

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Problems solved by technology

The method utilizes an effective view stability evaluation method to actively explore the re

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