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

An image retrieval and large-scale technology, applied in the field of computer vision, can solve problems such as consumption, retrieval accuracy limitation, and accuracy impact, and achieve the effect of reducing redundant information

Active Publication Date: 2018-11-30
UNIV OF ELECTRONIC SCI & TECH OF CHINA
View PDF11 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 imag

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep hash and GPU acceleration-based large-scale image retrieval method
  • Deep hash and GPU acceleration-based large-scale image retrieval method
  • Deep hash and GPU acceleration-based large-scale image retrieval method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0041] This embodiment proposes a large-scale image retrieval method based on deep hashing and GPU acceleration, wherein, such as figure 1 As shown, the deep hash network model of this embodiment is formed by stacking ResNet Building Blocks. Different from the traditional neural network structure, ResNet's Building Block structure includes a residual structure on the backbone and a short-cut structure on the branch. The short-cut structure is used to fuse low-level feature information with high-level feature information. , avoiding vanishing gradients and enabling deeper network hierarchies.

[0042] Such as figure 1 In structure A, for the original input, feature extraction is performed by two convolutional layers first, and then the original input and the output of the convolutional layer are used as input, and the Elewise layer passes through, which is also the standard Building Block structure of ResNet. In structure B, for the original input, feature extraction is perfo...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/30G06N3/04G06F9/48
CPCG06F9/4812G06N3/045
Inventor 段翰聪付美蓉黄子镭闵革勇谭春强
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products