Instance-level image search method based on multiple layers of feature representations

An image search and instance-level technology, applied in the field of image processing, can solve the problem that the generalization ability of the triplet loss function is not as good as that of softmax, and achieve the effect of enhancing the classification effect and good generalization performance

Active Publication Date: 2016-05-11
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
View PDF3 Cites 42 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the research, it was found that the triplet loss function has a good effect on the distinction between objects, which is not provided by the softmax loss function, but the generalization ability of the triplet loss function for features is not as good as that of softmax, so the two lack must

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
  • Instance-level image search method based on multiple layers of feature representations
  • Instance-level image search method based on multiple layers of feature representations
  • Instance-level image search method based on multiple layers of feature representations

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0039] Such as figure 1 As shown, a kind of instance-level image search based on multi-layer feature representation of the present invention includes:

[0040] 1. Multi-layer basic features

[0041] The network architecture is based on the existing classification neural network, such as VGG-16, GoogLeNet. Compared with GoogLeNet, VGG-16 has more parameters and takes longer to train the network, so in this article Mainly take GoogLeNet as an example to illustrate the method of multi-layer feature fusion.

[0042] The size of the GoogLeNet input image is 224x224, the input layer is connected with multiple convolutional layers, and 9 inception modules, the inception module is composed of small convolutions of 1x1, 3x3, 5x5, and finally the fully connected layer, softmax layer, the main fusion is the intermediate features extracted by part of the ...

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 relates to the technical field of computer vision, in particular to an instance-level image search method based on multiple layers of feature representations. According to the method, feature representations, on different layers, of images are learnt through a deep convolution neural network model for instance-level image search so that different images of the same article can be effectively found. A coding learning process is introduced on the basis of a traditional network model, and features from multiple convolution layers are automatically encoded, so that the extracted features are more robust, and influences of background and noise data on the features are reduced. Meanwhile, the features integrate local information and class information. The invention further discloses a loss function based on multiple tasks. By optimizing the function, the learnt features have good generalization performance, and the learnt features can be well used for distinguishing inter-class images from intra-class images of different things.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an instance-level image search method based on multi-layer feature representation. Background technique [0002] In the last ten years, instance-level image search has attracted a lot of attention. The problem can be simply described as giving an image of an object, allowing you to find different images of the same object or images close to the object from the data set. The rise of this problem is mainly due to a demand of consumers in online shopping. Consumers hope to upload an image of an object, and then the sales information of the same image can be displayed on the website. For example, Ali’s Pailitao is based on this demand. Developed, but in fact so far, the user experience is still not good, mainly due to the following reasons: [0003] Most of the images on the website are images with background or watermark, and the interference factors are large. At the same...

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): G06F17/30G06K9/62
CPCG06F16/5838G06F18/24
Inventor 徐勇顾一凡
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap