scholarly journals A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer

2019 ◽  
Vol 9 (11) ◽  
pp. 2173 ◽  
Author(s):  
Mingwei Wang ◽  
Lang Gao ◽  
Xiaohui Huang ◽  
Ying Jiang ◽  
Xianjun Gao
Keyword(s):  
Gabor Filter ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Feature Subset ◽  
Optimal Parameters ◽  
Practical Applications ◽  
Integrated Optimization ◽  
Ant Lion Optimizer ◽  
Ant Lion

Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.

2D(PC)2A Based Dimensionality Reduction of Textural Feature for Face Recognition with a Single Training Sample

2014 ◽  
Vol 596 ◽  
pp. 311-315
Author(s):  
Wen He Sun
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Gabor Filter ◽  
Texture Feature ◽  
Texture Features ◽  
Recognition System ◽  
Training Sample ◽  
Practical Applications ◽  
Feature Extract ◽  
2D Gabor Filter

Face recognition system works badly in practical applications because only single training sample image per person is stored in the system owing to hard collecting training samples. We present a novel face recognition scheme with single training sample using 2D Gabor filter and 2D(PC)2A under varying light conditions. Firstly, 2D texture feature extract with Gabor filter captures the properties of spatial localization, orientation selectivity, and spatial frequency selectivity to cope with the variations in illumination. Secondly, 2D(PC)2A is to extract statistical texture features under one training sample. Finally matrix-based similarity nearest neighbor classifier is used to classify a new face for recognition. Some experiments are implemented to testify the feasibility of the proposed scheme.

TEXTURE ANALYSIS USING LOCAL TEXTURE PATTERNS: A FUZZY LOGIC APPROACH

2011 ◽  
Vol 25 (05) ◽  
pp. 741-762 ◽  
Author(s):  
E. M. SRINIVASAN ◽  
K. RAMAR ◽  
A. SURULIANDI
Keyword(s):  
Texture Analysis ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Vital Role ◽  
Image Texture ◽  
Feature Extraction Method ◽  
Local Texture ◽  
Texture Model ◽  
Fuzzy Logic Approach

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis.

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 Ant Lion Optimizer for Texture Classification: A Moving Convolutional Mask

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61697-61705 ◽  
Author(s):  
Mingwei Wang ◽  
Lizhe Wang ◽  
Zhiwei Ye ◽  
Juan Yang
Keyword(s):  
Texture Classification ◽  
Ant Lion Optimizer ◽  
Ant Lion

Texture Feature Extraction for Color Images Based on Quaternion Representation

2013 ◽  
Vol 712-715 ◽  
pp. 2336-2340
Author(s):  
Chuan Bo Huang ◽  
Zi Ping Zhou
Keyword(s):  
Color Image ◽  
Gabor Filter ◽  
Texture Feature ◽  
Texture Features ◽  
Color Images ◽  
Color Image Processing ◽  
Texture Feature Extraction ◽  
Discrimination Power ◽  
Directional Information ◽  
Quaternion Representation

In order to get texture features of color image, we proposed a new algorithm which is adapted to extract the texture features for color images in this paper. Firstly, the proposed method adopt quaternion to color image processing that can represent the color image in a holistic manner and parallel processing the R, G and B components. Secondly, we can obtain the directional information and texture features by Quaternion Gabor Filter. The experimental results show that texture feature obtained by our method has good the discrimination power and classifing performance.

scholarly journals UNSUPERVISED TEXTURE CLASSIFICATION OF ENTROPY BASED LOCAL DESCRIPTOR USING K-MEANS CLUSTERING ALGORITHM

2011 ◽  
pp. 133-140 ◽  
Author(s):  
S. S. Sreeja Mole ◽  
L. Ganesan
Keyword(s):  
Texture Analysis ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Texture Classification ◽  
Clustering Algorithms ◽  
Texture Feature ◽  
Texture Features ◽  
Local Descriptor ◽  
Descriptor Approach

This paper presents an efficient approach for unsupervised Texture Segmentation and Classification, based on features extracted from entropy based local descriptor using K-means clustering with spatial information. The K- means clustering algorithm is commonly used in computer vision as a form of image segmentation. Texture analysis refers to a class of mathematical procedures and models that characterizes the spatial variations within imagery as a means of extracting information. Texture analysis may require the solution of two different problems first is Segmentation and Classification of a given image according to the different texture and second was for of a given texture with respect to a set of known textures. Based on the proposed concept, this paper describes the entropy based local descriptor using K-Means with spatial information approach. Experimental results show that the proposed framework performs very well compared to other clustering algorithms in all measured criteria. Spatial information has been effectively used for unsupervised texture classification for Brodatz of texture images. The model is not specifically confined to a particular texture feature. We tested this algorithm using other texture features. The proposed entropy based local descriptor approach gives good accuracy when compared with other methods.

scholarly journals The histogram of partitioned localized image textures

2017 ◽  
Author(s):  
◽  
Eric B. Brewster
Keyword(s):  
Object Detection ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Directional Pattern ◽  
Bag Of Words ◽  
Data Set ◽  
Pixel Location ◽  
Local Direction ◽  
Robustness To Noise

In the field of machine learning and pattern recognition, texture has been a prominent area of research. Humans are uniquely equipped to distinguish texture; however, computers are more equipped to automate the process. Computers accomplish this by taking images and extracting meaningful features that describe their texture. Some of these features are the Haralick texture features, local binary pattern (LBP), and the local direction pattern (LDP). Using the local directional pattern as an example, we propose a new texture feature called the histogram of partitioned localized image textures (HoPLIT). This feature utilizes a set of filters, not necessarily directional, and generates filter response vectors at every pixel location. These response vectors can be thought of as words in a document, which causes one to think of the bag-of-words model. Using the bag-of-words model, a codebook is created by partitioning a subset of response vectors from the entire data set. The partitions are represented by their mean texture and thus a word in the codebook. The mean textures now represent the keywords within the document, i.e. image. A histogram descriptor for an image is the frequency of pixels that belong to each partition. This feature is applied to a texture classification and segmentation problem as well as object detection. Within each problem domain, the HoPLIT feature is compared to the Haralick texture features, LBP, and LDP. The HoPLIT feature does very well classifying texture as well as segmenting large texture mosaics. HoPLIT also shows a surprising robustness to noise. Object detection proves to be slightly more difficult than texture classification for HoPLIT. However, it continues to outperform LBP and LDP.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

2021 ◽  
pp. 016173462199809
Author(s):  
Dhurgham Al-karawi ◽  
Hisham Al-Assam ◽  
Hongbo Du ◽  
Ahmad Sayasneh ◽  
Chiara Landolfo ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Empirical Evaluation ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Simple Majority ◽  
Ultrasound Scan ◽  
Histograms Of Oriented Gradients ◽  
The Individual ◽  
Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

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