Fuzzy Based Texton Binary Shape Matrix (FTBSM) for Texture Classification

Texton is a extensively applied approach for texture analysis. This technique shows a strong dependence on certain number of parameters. Unfortunately, each variation of values of any parameter may affect the texture characterization performance. Moreover, micro structure texton is unable to extract texture features which also have a negative effect on the classification task. This paper, deals with a new descriptor which avoids the drawbacks mentioned above. To address the above, the present paper derives a new descriptor called Fuzzy Based Texton Binary Shape Matrix (FTBSM) for clear variation of any feature/parameter. The proposed FTBSM are defined based on similarity of neighboring edges on a 3D7;3 neighborhood. With micro-structures serving as a bridge for extracting shape features and it effectively integrates color, texture and shape component information as a whole for texture classification. The proposed FTBSM algorithm exhibits low dimensionality. The proposed FTBSM method is tested on Vistex and Akarmarble texture datasets of natural images. The results demonstrate that it is much more efficient and effective than representative feature descriptors, such as logical operators and GLCM and LBP, for texture classification

Texture Analysis and Classification Based on Fuzzy Triangular Greylevel Pattern and Run-Length Features

Your Texture analysis is one of the most important techniques used in the analysis and interpretation of images, consisting of repetition or quasi repetition of some fundamental image elements. The present paper derived Fuzzy Triangular Greylevel Pattern (FTGP) to overcome the disadvantages of LBP and other local approaches. The FTGP is a 2 x 2 matrix that is derived from a 3 x 3 neighborhood matrix. The proposed FTGP scheme reduces the overall dimension of the image while preserving the significant attributes, primitives, and properties of the local texture. From each 3 x 3 matrix a Local Grey level Matrix (LGM) is formed by subtracting local neighborhoods by the gray value of its center. The 2 x 2 FTGP is generated from LGM by taking the average value of the Triangular Neighbor Pixels (TNP) of the 3 x 3 LGM. A fuzzy logic is applied to convert the Triangular Neighborhood Matrix (TNM) in to fuzzy patterns with 5 values {0, 1, 2, 3 and 4} instead of patterns of LBP which has two values {0, 1}. On these fuzzy patterns a set of Run Length features are evaluated for an efficient classification. The proposed method is experimented with wide variety of textures, and exhibited with a high classification rate. The proposed FTGP with run length features shown its supremacy and efficacy over the various existing methods in classification of textures

Класифікація станів печінки при дифузних захворюваннях на основі статистичних показників текстури ультразвукових зображень та МГУА

В статті розглянуто побудова класифікаторів стану норма-патологія по статистичним показникам текстури УЗ зображень при дифузних захворюваннях печінки. Запропоновано ряд нових показників для розрізнення текстури класів. Класифікатори побудовано методом групового урахування аргументів (МГУА) з використанням програмного забезпечення GMDH Shell DS. Робота виконана на даних, що було надано інститутом ядерної медицини і променевої діагностики НАМН України.The article deals with the construction of the norm-pathology states classifiers according to statistical features of the ultrasound images texture in diffuse liver diseases. A number of new features are proposed to distinguish the texture of classes. The classifiers are constructed using the Group Method of Data Handling (GMDH) using GMDH Shell DS software. The work was performed on data, had provided by the Nuclear Medicine and Radiation Diagnostics Institute of the National Academy of Medical Sciences of Ukraine.В статье рассмотрено построение классификаторов состояния норма-патология по статистическим показателям текстуры УЗ изображений при диффузных заболеваниях печени. Предложен ряд новых показателей для различения текстуры классов. Классификаторы построены методом группового учета аргументов (МГУА) с использованием программного обеспечения GMDH Shell DS. Работа выполнена на данных, предоставленных институтом ядерной медицины и лучевой диагностики НАМН Украины

Fuzzy clustering for content-based indexing in multimedia databases.

Yue Ho-Yin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 129-137).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Definition --- p.7Chapter 1.2 --- Contributions --- p.8Chapter 1.3 --- Thesis Organization --- p.10Chapter 2 --- Literature Review --- p.11Chapter 2.1 --- "Content-based Retrieval, Background and Indexing Problem" --- p.11Chapter 2.1.1 --- Feature Extraction --- p.12Chapter 2.1.2 --- Nearest-neighbor Search --- p.13Chapter 2.1.3 --- Content-based Indexing Methods --- p.15Chapter 2.2 --- Indexing Problems --- p.25Chapter 2.3 --- Data Clustering Methods for Indexing --- p.26Chapter 2.3.1 --- Probabilistic Clustering --- p.27Chapter 2.3.2 --- Possibilistic Clustering --- p.34Chapter 3 --- Fuzzy Clustering Algorithms --- p.37Chapter 3.1 --- Fuzzy Competitive Clustering --- p.38Chapter 3.2 --- Sequential Fuzzy Competitive Clustering --- p.40Chapter 3.3 --- Experiments --- p.43Chapter 3.3.1 --- Experiment 1: Data set with different number of samples --- p.44Chapter 3.3.2 --- Experiment 2: Data set on different dimensionality --- p.46Chapter 3.3.3 --- Experiment 3: Data set with different number of natural clusters inside --- p.55Chapter 3.3.4 --- Experiment 4: Data set with different noise level --- p.56Chapter 3.3.5 --- Experiment 5: Clusters with different geometry size --- p.60Chapter 3.3.6 --- Experiment 6: Clusters with different number of data instances --- p.67Chapter 3.3.7 --- Experiment 7: Performance on real data set --- p.71Chapter 3.4 --- Discussion --- p.72Chapter 3.4.1 --- "Differences Between FCC, SFCC, and Others Clustering Algorithms" --- p.72Chapter 3.4.2 --- Variations on SFCC --- p.75Chapter 3.4.3 --- Why SFCC? --- p.75Chapter 4 --- Hierarchical Indexing based on Natural Clusters Information --- p.77Chapter 4.1 --- The Hierarchical Approach --- p.77Chapter 4.2 --- The Sequential Fuzzy Competitive Clustering Binary Tree (SFCC- b-tree) --- p.79Chapter 4.2.1 --- Data Structure of SFCC-b-tree --- p.80Chapter 4.2.2 --- Tree Building of SFCC-b-Tree --- p.82Chapter 4.2.3 --- Insertion of SFCC-b-tree --- p.83Chapter 4.2.4 --- Deletion of SFCC-b-Tree --- p.84Chapter 4.2.5 --- Searching in SFCC-b-Tree --- p.84Chapter 4.3 --- Experiments --- p.88Chapter 4.3.1 --- Experimental Setting --- p.88Chapter 4.3.2 --- Experiment 8: Test for different leaf node sizes --- p.90Chapter 4.3.3 --- Experiment 9: Test for different dimensionality --- p.97Chapter 4.3.4 --- Experiment 10: Test for different sizes of data sets --- p.104Chapter 4.3.5 --- Experiment 11: Test for different data distributions --- p.109Chapter 4.4 --- Summary --- p.113Chapter 5 --- A Case Study on SFCC-b-tree --- p.114Chapter 5.1 --- Introduction --- p.114Chapter 5.2 --- Data Collection --- p.115Chapter 5.3 --- Data Pre-processing --- p.116Chapter 5.4 --- Experimental Results --- p.119Chapter 5.5 --- Summary --- p.121Chapter 6 --- Conclusion --- p.122Chapter 6.1 --- An Efficiency Formula --- p.122Chapter 6.1.1 --- Motivation --- p.122Chapter 6.1.2 --- Regression Model --- p.123Chapter 6.1.3 --- Discussion --- p.124Chapter 6.2 --- Future Directions --- p.127Chapter 6.3 --- Conclusion --- p.128Bibliography --- p.12