Image retrieval-oriented deep reinforcement redundancy elimination Hash algorithm
An image retrieval and depth technology, applied in still image data retrieval, still image data query, reasoning methods, etc., can solve the problems of hash code including redundancy, low sampling efficiency of hash algorithm, inability to maintain global similarity, etc. Achieve the effect of fast training speed, saving storage overhead, and saving computing overhead
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[0031] It can be seen from the background technology that there are two main defects in the existing hash algorithm for similarity image retrieval. First, most hashing algorithms are trained in small batches, which has low sampling efficiency and cannot maintain global similarity information. Second, the hash codes generated by most hash algorithms contain some redundant bits or even harmful bits. Removing these bits can not only improve the retrieval accuracy, but also reduce computing and storage costs. Therefore, this embodiment addresses the above two problems by using hash code reasoning based on block calculation and hash code de-redundancy based on deep reinforcement learning respectively.
[0032] In this embodiment, the label information is first used to construct a similarity matrix:
[0033]S=min(YY T ,1)×2-1 (6)
[0034] where, S∈{-1,+1} n×n , S ij =+1 means that the i-th image is similar to the j-th image; S ij = -1 means that the i-th image is not similar t...
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