A Method of the Extraction of Texture Feature

Authors:
Haifang Li;Lihuan Men;Junjie Chen
Affiliations:
College of Computer and Software, Taiyuan University of Technology, Taiyuan, China 030024;College of Computer and Software, Taiyuan University of Technology, Taiyuan, China 030024;College of Computer and Software, Taiyuan University of Technology, Taiyuan, China 030024
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

Citing 5
Cited 0

Local Grayvalue Invariants for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures

Communications of the ACM
Computer Analysis of Visual Textures

Computer Analysis of Visual Textures
Defining Image Content with Multiple Regions-of-Interest

CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Detection of Ellipses by a Modified Hough Transformation

IEEE Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to understand the emotional information of the color image, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the `semantic gap' between the visual features and the richness of human perception. In this paper, we firstly get the ROI using the Eye tracker and divide every image into two regions including Regions of Interest (ROI) and Non- Regions of Interest (Non-ROI). Secondly, we use the analytical hierarchy process (AHP) to provide a systematical way to evaluate the fit weights of ROI and Non-ROI. Finally, using the improved GLCM, we extract the texture feature of the two regions including ROI and Non-ROI, and get the whole texture feature. The algorithm is tested that the average detection rate of the proposed method is up to the same method using GLCM.