Texture Segmentation by Genetic Programming

2008 ◽  
Vol 16 (4) ◽  
pp. 461-481 ◽  
Author(s):  
Andy Song ◽  
Vic Ciesielski
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Texture Segmentation ◽  
Segmentation Method ◽  
Texture Feature Extraction ◽  
Powerful Approach ◽  
Computation Algorithms

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

Adaptive Texture Feature Extraction with Application to Ultrasonic Image Analysis

1998 ◽  
Vol 20 (2) ◽  
pp. 132-148 ◽  
Author(s):  
H.J. Huisman ◽  
J.M. Thijssen
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Texture Features ◽  
Small Data ◽  
Data Sets ◽  
Large Set ◽  
Texture Feature Extraction ◽  
Feed Forward Neural Network ◽  
Feature Extraction Method ◽  
Optimal Feature

Computer texture analysis methods use texture features that are traditionally chosen from a large set of fixed features known in literature. These fixed features are often not specifically designed to the problem at hand, and as a result they may have low discriminative power, and/or may be correlated. Increasing the number of selected fixed features is statistically not a good solution in limited data environments such as medical imaging. For that reason, we developed an adaptive texture feature extraction method (ATFE) that extracts a small number of features that are tuned to the problem at hand. By using a feed-forward neural network, we ensure that even nonlinear relations are captured from the data. Using extensive, repeated synthetic ultrasonic images, we compared the performance of ATFE with the optimal feature set. We show that the ATFE method is capable of robust operation on small data sets with a performance close to that of the optimal feature set. Another experiment confirms that our ATFE is capable of capturing nonlinear relations from the dataset. We conclude that our method can improve performance in practical, limited dataset situations where an optimal fixed feature set can be hard to find.

scholarly journals A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

2019 ◽  
Vol 11 (14) ◽  
pp. 1636 ◽  
Author(s):  
Xudong Lai ◽  
Jingru Yang ◽  
Yongxu Li ◽  
Mingwei Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Land Management ◽  
Extraction Method ◽  
Point Cloud ◽  
Texture Feature ◽  
Texture Features ◽  
Extraction Methods ◽  
Building Extraction ◽  
Texture Feature Extraction

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

Texture Feature Extraction and Classification of Pork Loin Ultrasonography Images

2013 ◽  
Vol 791-793 ◽  
pp. 1978-1981
Author(s):  
Tao Li ◽  
Jian Xun Zhang ◽  
Quan Sun
Keyword(s):  
Feature Extraction ◽  
Ultrasound Image ◽  
Texture Feature ◽  
Texture Features ◽  
Fat Content ◽  
Image Processing Algorithm ◽  
Texture Feature Extraction ◽  
Pork Loin ◽  
Pork Fat

The method of texture feature extraction and classification of pork loin B ultrasound image is proposed, which can be applied to the computer-aided judgment the pork loin fat content of pork loin. 5 texture features which is based on the texture of the co-occurrence matrix are extracted from the B ultrasound image of pork loin according to the digital image processing algorithm. Using the correlation analysis method to select the key texture extraction in the first step. Then,the classification is realized based on the BP neural network. The train set and test set are randomly chosen from 135 cases. Tests performed show that the proposed method result in a high classification accuracy, which will provide the researcher a valuable opinion on the pork fat content detection.

Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

2020 ◽  
Vol 39 (4) ◽  
pp. 4847-4858
Author(s):  
Lei Wang ◽  
Jinhai Sun ◽  
Tuojian Li
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Feature Recognition ◽  
Texture Feature ◽  
Texture Features ◽  
Spatial Domain ◽  
Image Texture ◽  
Support Vector ◽  
Texture Feature Extraction

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval

2011 ◽  
pp. 44-67 ◽  
Author(s):  
Gang Zhang ◽  
Z. M. Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Image ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Texture Feature Extraction ◽  
Medical Image Retrieval ◽  
Image Retrieval System ◽  
Occurrence Matrix

Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first proposes a framework of content-based medical image retrieval system. It then analyzes the important texture feature extraction and description methods further, such as the co-occurrence matrix, perceptual texture features, Gabor wavelet, and so forth. Moreover, the chapter analyzes the improved methods for these methods and demonstrates their application in content-based medical image retrieval.

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