Automatic labeling method for multi-view images

An image automatic labeling, multi-view technology, applied in the field of image processing, can solve the problem of ignoring the differences of different views, and achieve the effect of improving the labeling performance and the labeling performance.

Inactive Publication Date: 2018-06-22
NORTH CHINA UNIVERSITY OF TECHNOLOGY
View PDF3 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Multi-view sparse coding is an important branch of automatic image annotation, but in existing methods, each view often shares the same sparse coefficients, ignoring the differences of different views

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
  • Automatic labeling method for multi-view images
  • Automatic labeling method for multi-view images
  • Automatic labeling method for multi-view images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many of the details listed in the specification are only for readers to have a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be implemented even without these specific details.

[0044] A method for automatic labeling of multi-view images proposed in this embodiment, its flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0045] (1) Set the semantic labels and various visual features of the marked images as multiple views, input them into the multi-view sparse model for training and learning, and obtain the view dictionaries and the weight factors of each view. Wherein, each view dictionary includes ...

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 an automatic labeling method for multi-view images. The automatic labeling method for multi-view images comprises the following steps: (1) setting semantic labels of labeled images and various visual features as multiple views, inputting the multiple views into a multi-view sparse model and carrying out training learning to obtain various view dictionaries and various viewweight value factors; (2) inputting multiple visual features of images to be labeled; (3) reconstituting the images to be labeled sparsely by the various view dictionaries and the various view weightvalue factors, and calculating to obtain sparse reconstituted coefficients of label views; (4) multiplying the label view dictionaries with the sparse reconstituted coefficients of the label views toobtain the scores of the semantic labels of the images to be labeled; and (5) arranging the scores from high to low, and labeling the images to be labeled by using top five semantic labels. The automatic image labeling performance of a computer is improved, and precision ratio and recall ratio of automatic labeling are improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a multi-view image automatic labeling method. Background technique [0002] With the rapid development of multimedia information technology, the effective management and retrieval of massive image databases has become an urgent problem to be solved. Currently, text-based image retrieval methods are still an important method for many image search engines to retrieve relevant images. Therefore, the accuracy and efficiency of image retrieval will be greatly improved if keywords that reflect its semantic content are assigned to images in advance. Automatic image annotation is to let the computer complete this task automatically and intelligently. It uses labeled image sets or other available prior information to automatically learn the mapping relationship between semantic concept space and visual feature space, and uses this relationship to label images with unknown semantics. The...

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
CPCG06F16/532G06F16/5838G06F18/2136
Inventor 臧淼
Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY
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