Deep hash and GPU acceleration-based large-scale image retrieval method

Abstract

The invention discloses a deep hash and GPU acceleration-based large-scale image retrieval method. According to the method, classification loss functions and comparison loss functions are combined onthe basis of a hash method for picture pairs by adoption of a multitask deep learning mechanism, and in the quantification process, similarities between the picture pairs are kept, semantic information of pictures is kept as much as possible, and classification tasks and quantification tasks carry out mutual guiding and learning; and meanwhile, a full-connection layer of a quantification network is replaced by a local connection module, so that redundant information between features is decreased. According to the method, deeper networks are designed and realized, and the deeper networks can obtain feature expressions in general. On the basis of Han / Ming sorting, a GPU-based multilayer parallel retrieval method is realized. The method is capable of improving the retrieval precision and achieving an effect of delaying the retrieval of a single image in a million-scale image library to be 0.8ms.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a large-scale image retrieval method based on deep hashing and GPU acceleration. Background technique [0002] In recent years, with the rapid development of the Internet and various multimedia devices, it has become more and more convenient to obtain images from the Internet. At the same time, the current social network has become more and more popular, such as Facebook, QQ, etc. According to incomplete According to statistics, the number of pictures added on the Internet every month is at the level of one billion. Coincidentally, as people become more and more accustomed to online shopping, tens of billions of pictures have also accumulated in the background systems of major e-commerce platforms. For these massive data, how to organize and effectively use these data has become an urgent problem to be solved. Therefore, image retrieval technology has attracted much attention and h...

AI Technical Summary

Problems solved by technology

If you need to ensure a certain speed, you need to consume more GPUs, which will double the cost

[0009] (2) The image retrieval method using the hash algorithm has good data scalability and can cope with a variety of data sets after the function is obtained, but it cannot use the deep information of the image label, and the retrieval accuracy is limited.

[0010] (3) In the image retrieval method using the deep hash algorithm, the use of triples will lead to slower convergence during the convergence training process, and the final effect is largely affected by the selection of triples, but the triplets How to choose is still a question to be studied; and the activation function of sigmoid or tanh is relatively large, which has an impact on accuracy

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