Automatic marking method for natural scene image

A natural scene image and automatic labeling technology, applied in the field of computer vision, can solve the problems of crosstalk between foreground objects and background classification, color texture features, large differences, and inconsistent classification, so as to improve the classification accuracy and the overall accuracy of pixel labeling. Classification accuracy, significant effect

Active Publication Date: 2016-09-07
江苏优利信科技有限公司
View PDF6 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problem of low classification accuracy of complex foreground objects in image labeling, the present invention proposes an automatic labeling method for natural scene images, whi...

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 marking method for natural scene image
  • Automatic marking method for natural scene image
  • Automatic marking method for natural scene image

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The specific embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0033] Such as figure 1 As shown, the present invention discloses a method for automatic labeling of natural scene images. It should be noted that the core step of the present invention is to embed saliency detection prior information in the CRF model. The description of the specific implementation mainly focuses on step 3 , the undisclosed content of steps 1, 2 and step 4 can be implemented using existing technologies, and the specific description is as follows:

[0034] 1. Extract image features:

[0035] Feature extraction is one of the important contents of visual tasks such as target recognition and image understanding. It often characterizes the pixel (or superpixel) by composing the color of the pixel and the surrounding texture and other eigenvalues ​​obtained by filtering into a vector. Discriminative feature extraction is the basis for o...

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 marking method for a natural scene image, and belongs to the field of computer vision. The method comprises the following steps that image features are extracted; an original image is segmented by adopting an unsupervised algorithm so that a super-pixel graph is generated; modeling of a pixel marking model is performed through CRF and significant prior information is embedded in the model; and the model is solved and pixel marking is realized. The CRF is adopted to act as a basic model, the significant detection prior information is introduced in the CRF model, and separation of a foreground target and a background can be realized through significant detection and a universal connection association relation between the super-pixels is constructed in a foreground target area. The significant detection prior information is introduced so that the classification precision of the foreground target in the image can be effectively enhanced. Meanwhile, the problem of classification "crosstalk" of the foreground and the background can be effectively solved by the separation of the foreground area and the background area. Therefore, the overall classification precision of pixel marking can be effectively enhanced by the method, and the method has substantial effect for the scenes of relatively complex foreground target profiles and subareas of highly different colors and textures.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to an automatic labeling method for natural scene images. Background technique [0002] In recent years, image understanding in computer vision has developed rapidly and has been widely used in many fields, and has attracted extensive attention from scholars. Existing scene image labeling algorithms often use the undirected graph model conditional random field CRF (Conditional Random Field) as the basic framework, and achieve pixel labeling by introducing local smoothness, position, co-occurrence, mutual exclusion and other contextual prior information into the conditional random field CRF . Although the introduction of existing contextual prior information has enhanced the model description ability to a certain extent, there are still deficiencies, and it is difficult to effectively improve the classification accuracy, especially the classification accuracy of foreground objects. A...

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): G06T7/00
CPCG06T2207/20081
Inventor 杨明李志青
Owner 江苏优利信科技有限公司
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