Supervised depth hashing fast picture retrieval method and system

Abstract

The present invention provides a supervised depth hashing fast picture retrieval method and system. The method comprises: constructing a depth convolution neural network H" for fast image retrieval; after the pictures in the library are sequentially input into the depth convolution neural network H", obtaining real value features, obtaining hash codes after the quantization operation and storing the codes locally; and inputting each query picture q into the depth convolution neural network H", quantifying the picture to obtain the hash code h (q), calculating the Hamming distance between the hash code h (q) and all hash codes stored locally, taking the small Hamming distance as that the similarity is high, sorting the pictures by taking the order, and finally returning the corresponding number of pictures with the highest similarity according to the requirement of the retrieval number. According to the method and system provided by the present invention, based on the existing depth neural network, the learning of the picture feature expression is carried out by using the triple tag data, and a triple quantization loss function is used to construct the supervised depth hashing model, so that fast and accurate image retrieval can be realized.

Description

technical field [0001] The invention relates to the fields of computer vision and image processing, in particular to a supervised deep hash fast image retrieval method and system based on a triple quantization loss function. Background technique [0002] With the rapid development of information technology, massive amounts of data are continuously generated, and the scale of image data is increasing exponentially. The huge amount of data makes direct retrieval of similar images bring great time and space overhead. Therefore, how to quickly retrieve similar images from massive images has become an urgent problem to be solved. Hashing has become a common solution as a way to map images into low-dimensional binary codes. In recent years, the deep convolutional neural network has developed rapidly, and the deep hashing method based on it has shown great potential in the field of fast image retrieval. In particular, supervised deep hashing methods have received extensive attent...

AI Technical Summary

Problems solved by technology

[0004] However, the defect of this method is that the loss of semantic information caused by reducing quantization is not equivalent to learning approximately binarized image features; on the contrary, this is a very strong constraint for network learning tasks, making the learned features themselves Only contains very little semantic information, in other words, the quantization loss function designed by Liu et al. leads to a suboptimal solution

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