Large-Scale Image Retrieval Method Based on Deep Hashing and GPU Acceleration

Abstract

The invention discloses a large-scale image retrieval method based on deep hashing and GPU acceleration. The method is based on the hashing method of image pairs, adopts a multi-task deep learning mechanism, and combines a classification loss function with a comparison loss function. During the quantization process, While retaining the similarity between the image pairs, the semantic information of the image itself is preserved as much as possible, and the classification task and the quantization task guide each other to learn; at the same time, the local connection module is used to replace the fully connected layer of the quantization network to reduce the relationship between features. redundant information. A deeper network is designed and implemented, and the deep network can usually get a good feature representation. On the basis of Hamming sorting, a GPU-based multi-level parallel retrieval method is realized. The invention not only improves the retrieval accuracy, but also can achieve the effect of 0.8ms delay for a single retrieval in a million-scale image database.

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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