Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Multi-task layered image retrieval method based on depth self-coding convolution neural network

A convolutional neural network and image retrieval technology, which is applied in the direction of biological neural network models, neural architectures, and special data processing applications, can solve the problems of slow large-scale image retrieval, accelerated retrieval speed, low level of automation and intelligence, etc. , to achieve the effect of meeting image retrieval requirements, speeding up retrieval, and reducing memory dependence

Active Publication Date: 2018-02-09
ZHEJIANG UNIV OF TECH
View PDF9 Cites 105 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] Aiming at the problems of low level of automation and intelligence, lack of deep learning, difficulty in obtaining accurate retrieval results, large storage space consumption of retrieval technology, slow retrieval speed and difficulty in meeting image retrieval requirements in the era of big data in the existing image search technology , the present invention proposes an end-to-end image retrieval method through layered depth search based on a deep self-encoding convolutional neural network, and uses a deep learning method to improve the level of automation and intelligence in the retrieval system while enabling image recognition and feature acquisition The perfect combination of retrieval efficiency and retrieval efficiency enables the entire retrieval system to obtain accurate retrieval results. The use of sparse coding reduces the system's dependence on memory and speeds up retrieval, thus meeting the image retrieval requirements in the era of big data.
[0014] To realize the above invention, several core problems must be solved: 1) Aiming at the difficult problem of image feature extraction, using the powerful feature representation ability of deep self-encoding convolutional neural network to realize feature adaptive extraction; 2) Aiming at large-scale image retrieval For the problem of slow speed, design a multi-task layering method, use the query image to quickly compare the image in the database; 3) for the multi-target image scene semantic retrieval, design a secondary screening algorithm for the region of interest to multi-target image Detection and segmentation; 4) Using the advantages of end-to-end deep networks, design an end-to-end deep self-encoding convolutional neural network to integrate detection, recognition, and feature extraction into one network

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
  • Multi-task layered image retrieval method based on depth self-coding convolution neural network
  • Multi-task layered image retrieval method based on depth self-coding convolution neural network
  • Multi-task layered image retrieval method based on depth self-coding convolution neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0081] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention.

[0082] refer to Figure 1 to Figure 6 , a multi-task hierarchical image retrieval method based on deep self-encoding convolutional neural network, such as Figure 6 As shown, the input retrieved image first passes through the shared module of the convolutional neural network, and then is sent to the interest module to screen out the position of the rough region of interest in the image, and then sent to the fast model based on the interest module for the secondary screening of the region of interest. The precise position of the target in the image can be obtained by visual segmentation detection and positioning. Through the method of deep learning, the sparse hash code of the entire image can be obtained for rough retrieval, and the region-aware semantic features of the maximu...

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 multi-task layered image retrieval method based on a depth self-coding convolution neural network. The method is characterized by mainly comprising a multi-task end-to-end convolution neural network for deep learning and training recognition, a rapid visual segmentation detection and positioning method of a region-of-interest secondary screening module based on an RPN network, a coarse search of a full-graph sparse hash code, an area sensing semantic feature and matrix h accurate comparison and search based on the maximum response, and a region-of-interest selectivitycomparison algorithm. According to the method, the end-to-end training can be achieved, the interest region with higher quality can be automatically selected, the automation degree and the intelligent level of search by images can be effectively improved, and the image retrieval requirements of the big data age can be met by using little storage space at a high search speed.

Description

technical field [0001] The present invention relates to the application of computer vision, pattern recognition, information retrieval, multi-task learning, similarity measurement, deep self-encoding convolutional neural network and deep learning technology in the field of image retrieval, in particular to a deep self-encoding convolutional neural network A Multi-Task Hierarchical Image Retrieval Method. Background technique [0002] The purpose of image retrieval is to retrieve similar images by analyzing the input query image content, providing users with a search technology for image information retrieval, which includes image processing, computer vision, multi-task learning, pattern recognition and cognitive psychology and other disciplines. Its related technologies mainly include image representation acquisition and similarity measurement. In the context of the big data era, image retrieval, video detection, Internet, shopping search engines and other fields have been...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N3/04G06K9/32
CPCG06F16/583G06V10/25G06N3/045
Inventor 何霞汤一平王丽冉陈朋袁公萍金宇杰
Owner ZHEJIANG UNIV OF TECH
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
Eureka Blog
Learn More
PatSnap group products