PIXEL-BASED TEXTURE CLASSIFICATION BY INTEGRATION OF MULTIPLE FEATURE EXTRACTION METHODS EVALUATED OVER MULTISIZED WINDOWS

2007 ◽  
Vol 21 (07) ◽  
pp. 1159-1170 ◽  
Author(s):  
DOMENEC PUIG ◽  
MIGUEL ANGEL GARCIA
Keyword(s):  
Feature Extraction ◽  
Texture Classification ◽  
Texture Features ◽  
Extraction Methods ◽  
Input Image ◽  
Integration Strategy ◽  
Multiple Feature

This paper presents a pixel-based texture classifier oriented to the identification of texture models that can be present in an input image, given a set of models known in advance. The proposed methodology is based on the integration of texture features generated by texture methods that belong to different families, which are evaluated over multiple windows of different sizes. This is a novelty with respect to the current texture classifiers, which are based on specific families of texture methods evaluated over single windows of a size defined empirically. Experiments show that this integration strategy produces better results than classical texture classifiers based on specific families of texture methods.

scholarly journals GPU-accelerated image segmentation based on level sets and multiple texture features

2020 ◽  
Author(s):  
Daniel Reska ◽  
Marek Kretowski
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Level Set ◽  
High Performance ◽  
Texture Features ◽  
Extraction Methods ◽  
Multi Stage ◽  
Multiple Feature ◽  
Feature Extraction And Selection ◽  
Contour Evolution

Abstract In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

Feature Extraction of Melanoma Data using Machine Learning Techniques

2021 ◽  
pp. 5352-5360
Author(s):  
R.Veeralakshmi, Dr.K.Merriliance
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Skin Diseases ◽  
Region Of Interest ◽  
Texture Features ◽  
Extraction Methods ◽  
Input Image ◽  
The Body ◽  
Machine Learning Techniques ◽  
Segmentation Technique

In our body the skin is the largest organ, it protects from injury, infection and also helps to maintain the temperature of the body. Melanoma Skin cancer is one of the most dangerous skin diseases and it is caused by an uncontrolled growth of abnormal skin cells, by ultraviolet radiation from sunshine. Melanoma is more common among white skins such as Americans than in darker skins. The digital lesion images have been analyzed based on image acquisition, pre-processing, and image segmentation technique. The image segmentation technique is applied to easily identify the affected portion in the skin input image. The images are enhanced using morphological filters and sharpen region of interest in an image, enhancement method enhanced the non-uniform background illumination and converts the input image into a binary image too easy to identify foreground objects. The mole of melanoma is segmented from the background using Active Contour algorithm. After that, the feature extraction methods such as Kernel PCA, SIFT are used to extract melanoma affected area in an image based on their intensity and texture features.

INVESTIGATING LONG-TERM EFFECTS OF FEATURE EXTRACTION METHODS FOR CONTINUOUS EMG PATTERN CLASSIFICATION

2012 ◽  
Vol 11 (04) ◽  
pp. 1250028 ◽  
Author(s):  
ANGKOON PHINYOMARK ◽  
PORNCHAI PHUKPATTARANONT ◽  
CHUSAK LIMSAKUL
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Pattern Classification ◽  
Time Domain ◽  
Time Scale ◽  
Extraction Methods ◽  
Feature Sets ◽  
Scale Feature ◽  
Multiple Feature

Based on recent advances in modern multifunction myoelectric control devices, a combination of effective feature extraction and classification methods is required to enhance the high classification performance, especially in accuracy viewpoint. However, for realizing practical applications of myoelectric control, the effect of long-term usage or reusability is one of the challenging issues that should be more carefully considered, whereas only a few works have investigated this effect in recent. In this study, the behavior of the state-of-the-art multiple feature extraction methods was investigated with the fluctuating electromyography (EMG) signals recorded during four different days with a large number of trials and subjects. To this end, seven multiple feature sets were compared consisting features based on time domain and time-scale representation. Two major points were emphasized: (1) the optimal robust feature set for continuous (both transient and steady-state signals) EMG pattern classification and (2) the effect of fluctuating EMG signals with feature extraction methods for long-term usage. From the classification results, time domain feature sets yielded better performance than time-scale feature sets. The classification accuracies of the time-domain-feature sets had always achieved above 80% by using linear discriminant analysis (LDA) as a classifier and uncorrelated LDA (ULDA) as a dimensionality reduction, whereas the classification accuracies of the time-scale-feature sets were lower than 70% for the fluctuating EMG signals. The effect of dimensionality reduction for the classification of fluctuating EMG signals was also discussed.

scholarly journals fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network

2021 ◽  
Vol 12 (1) ◽  
pp. 81-105 ◽  
Author(s):  
Senuri De Silva ◽  
Sanuwani Udara Dayarathna ◽  
Gangani Ariyarathne ◽  
Dulani Meedeniya ◽  
Sampath Jayarathna
Keyword(s):  
Feature Extraction ◽  
Low Frequency ◽  
Extraction Methods ◽  
Functional Magnetic Resonance ◽  
Hyperactivity Disorder ◽  
Specificity And Sensitivity ◽  
Multiple Feature ◽  
Clinical Data Analysis ◽  
Extraction Model ◽  
Neuroimaging Techniques

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to obtain the specificity and sensitivity. This helps to determine the best features for ADHD classification using convolutional neural networks (CNN). The methodology using fALFF and ReHo resulted in an accuracy of 67%, while SBC gained an accuracy between 84% and 86% and sensitivity between 65% and 75%.

Wavelets-Based Feature Extraction for Texture Classification

2010 ◽  
Vol 97-101 ◽  
pp. 1273-1276 ◽  
Author(s):  
Gang Yu ◽  
Ying Zi Lin ◽  
Sagar Kamarthi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Energy Content ◽  
Texture Classification ◽  
Wavelet Coefficient ◽  
Discrimination Performance ◽  
Extraction Methods ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Feature Extraction Method

Texture classification is a necessary task in a wider variety of application areas such as manufacturing, textiles, and medicine. In this paper, we propose a novel wavelet-based feature extraction method for robust, scale invariant and rotation invariant texture classification. The method divides the 2-D wavelet coefficient matrices into 2-D clusters and then computes features from the energies inherent in these clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors are input to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to that obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

Relevant Feature Subset Selection from Ensemble of Multiple Feature Extraction Methods for Texture Classification

2015 ◽  
Vol 5 (1) ◽  
pp. 48-65
Author(s):  
Bharti Rana ◽  
Akanksha Juneja ◽  
Ramesh Kumar Agrawal
Keyword(s):  
Feature Extraction ◽  
Texture Classification ◽  
Subset Selection ◽  
Computation Time ◽  
Extraction Methods ◽  
Classification Performance ◽  
Feature Subset Selection ◽  
Classification Error ◽  
Second Phase ◽  
Feature Subset

Performance of texture classification for a given set of texture patterns depends on the choice of feature extraction technique. Integration of features from various feature extraction methods not only eliminates risk of method selection but also brings benefits from the participating methods which play complimentary role among themselves to represent underlying texture pattern. However, it comes at the cost of a large feature vector which may contain redundant features. The presence of such redundant features leads to high computation time, memory requirement and may deteriorate the performance of the classifier. In this research workMonirst phase, a pool of texture features is constructed by integrating features from seven well known feature extraction methods. In the second phase, a few popular feature subset selection techniques are investigated to determine a minimal subset of relevant features from this pool of features. In order to check the efficacy of the proposed approach, performance is evaluated on publically available Brodatz dataset, in terms of classification error. Experimental results demonstrate substantial improvement in classification performance over existing feature extraction techniques. Furthermore, ranking and statistical test also strengthen the results.

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.

scholarly journals Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Extraction Methods ◽  
Input Image ◽  
Image Features ◽  
Local Feature ◽  
Almost All ◽  
Local Feature Extraction ◽  
Global And Local

© 2018 IEEE. Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.

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.

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