Cityscape image semantic segmentation method based on multi-feature fusion and Boosting decision forest

A multi-feature fusion and semantic segmentation technology, applied in the field of image processing, can solve problems such as increasing the amount of calculation

Inactive Publication Date: 2014-08-13
ZHEJIANG GONGSHANG UNIVERSITY
View PDF3 Cites 45 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since each pixel needs to be judged, the pixel-level target recognition algorithm will also generate a huge amount of data calculations. At the same time, most of the neighborhoods of adjacent pixels overlap, so the extracte...

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
  • Cityscape image semantic segmentation method based on multi-feature fusion and Boosting decision forest
  • Cityscape image semantic segmentation method based on multi-feature fusion and Boosting decision forest
  • Cityscape image semantic segmentation method based on multi-feature fusion and Boosting decision forest

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0074] The method of the invention includes three parts: superpixel segmentation of the target, representation of target features and target recognition. We use the superpixel block as the basic unit of processing, fuse various effective features, and extract nonlinear features through PCA dimensionality reduction as a training model, and then use the fusion features as the input features of the improved random forest classifier to perform There is supervised training and learning, and finally realizes the classification and recognition of the target, and at the same time carries out semantic annotation.

[0075] The semantic segmentation method of the street view image based on multi-feature fusion and Boosting decision forest is characterized in that the method comprises the following steps:

[0076] Step 1, perform superpixel segmentation on the image; in view of the complex characteristics of the objects contained in the street view image, use simple linear iterative clust...

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

A cityscape image semantic segmentation method based on multi-feature fusion and Boosting decision forest includes the following steps of carrying out super-pixel segmentation on images, carrying out multi-feature extraction, carrying out feature fusion and carrying out training learning and classification recognition. The method effectively integrates 2D features and 3D features and remarkably improves recognition rates of targets. Compared with the prior art, segmentation results are consistent, connectivity is good, edge positioning is accurate, a Boosting decision forest classification mechanism is introduced, and stability of target classification is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for semantic segmentation of street view images based on multi-feature fusion and Boosting decision forest. Background technique [0002] Image segmentation is the technology and process of dividing an image into multiple regions with similar characteristics, and it is an important issue in image processing. The features here can be grayscale, color, texture, etc. of pixels, and the predefined target can be a single area or multiple areas. Image segmentation is not only the basis of object representation, but also has an important impact on feature quality, and can transform the original image into a more abstract form, making higher-level image analysis and understanding possible. Image understanding in computer vision, such as object detection, object feature extraction, and object recognition, etc., all rely on the quality of image segmentation. Image proce...

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): G06K9/62G06K9/46
Inventor 王慧燕付建海
Owner ZHEJIANG GONGSHANG UNIVERSITY
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