Image marking method based on multi-view and semi-supervised learning mechanism

A semi-supervised learning and image labeling technology, applied in computer parts, character and pattern recognition, special data processing applications, etc.

Active Publication Date: 2014-07-30
NANJING UNIV OF POSTS & TELECOMM
View PDF5 Cites 30 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies of existing image labeling methods, and provide an image labeling method based on multi-vie

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
  • Image marking method based on multi-view and semi-supervised learning mechanism
  • Image marking method based on multi-view and semi-supervised learning mechanism
  • Image marking method based on multi-view and semi-supervised learning mechanism

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach

[0025] A preferred embodiment of the image labeling method based on the multi-view learning mechanism and the semi-supervised learning mechanism of the present invention specifically includes the following steps:

[0026] Step 1: Learning process of multi-view classifier based on uncorrelated visual features

[0027] 1. Extract enough independent views such as wavelet texture, color histogram and edge direction histogram from the image;

[0028] 2. To mark the set of images {x 1 ,x 2 ,...,x l ,...,x L} train the Vth view classifier h v :

[0029] h v :x lv →y k ,l∈(1,L),v∈(1,V),y k ∈ Y (1)

[0030] Step 2: Multi-view classifier optimization process based on labeled samples and pseudo-labeled samples with higher confidence

[0031] let p uv k Denotes the pseudo-labeled sample x in the vth view (L+u) The probability value belonging to the kth label:

[0032] p uv k = p u ...

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 image marking method based on a multi-view and semi-supervised learning mechanism. The method comprises the following steps that a multi-view classifier learning process based on irrelevant view characteristics is carried out; a multi-view classifier optimization process based on marking samples and pseudo marking samples with higher reliability is carried out; a multi-view marking process based on a maximum entropy voting principle and the marking correlation is carried out. The performance of the multi-view and semi-supervised image marking method provided by the invention is obviously superior to the performance of other schemes, the method has the main ideal that firstly, mutually irrelevant views are utilized for training a plurality of independent classifiers, then, initial marking samples and pseudo marking samples are utilized for optimizing the view classifier, and finally, proper semantic annotation is allocated to each unmarked image on the basis of the maximum entropy voting principle and the correlation between all marks.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to an image labeling method based on a multi-view and semi-supervised learning mechanism. Background technique [0002] As more and more digital images appear on the network, personal computers, and digital acquisition devices, the desire to effectively organize and manage such massive image information by using content-based analysis techniques is also growing stronger. Among them, image annotation is the most important and critical step to realize content-based image indexing, retrieval and other related applications. Its purpose is to establish an accurate correspondence between the underlying visual information and the high-level language description. [0003] In recent years, researchers have proposed various solutions to image labeling. For example, embedded deep belief network method, covariance discriminant method, bilinear deep learning method, local and ...

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): G06F17/30G06K9/66
CPCG06F18/2111
Inventor 朱松豪陈玲玲李向向
Owner NANJING UNIV OF POSTS & TELECOMM
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