Texture Classification Using Ridgelet Transform (original) (raw)
Related papers
Texture Classification with Feature Analysis Using Wavelet Approach: A Review
2015
Textures play important roles in many image processing applications, since images of real objects often do not exhibit regions of uniform and smooth intensities, but variations of intensities with certain repeated structures or patterns, referred to as visual texture. The textural patterns or structures mainly result from the physical surface properties, such as roughness or oriented structured of a tactile quality. It is widely recognized that a visual texture, which can easily perceive, is very difficult to define. The difficulty results mainly from the fact that different people can define textures in applications dependent ways or with different perceptual motivations, and they are not generally agreed upon single definition of texture. The development in multi-resolution analysis such as Local Binary Pattern and wavelet transform help to overcome this difficulty.
Wavelet Based Features for Texture Classification
2006
This paper presents a feature extraction algorithm using wavelet decomposed images of an image and its complementary image for texture classification. The features are constructed from the different combination of sub-band images. These features offer a better discriminating strategy for texture classification and enhance the classification rate. In our study we have used the Euclidean distance measure and the minimum distance classifier to classify the texture. The experimental results demonstrate the efficiency of the proposed algorithm.
Texture classification using wavelet transform
Pattern Recognition Letters, 2003
Today, texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image data bases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of ...
Texture Classification with Feature Analysis : A Wavelet Based Approach
2015
Texture has been widely used in human life since it provides useful information that appeared on the surface of every object. The most common use of texture is to help everyone to identify different objects in daily life. Texture is also often involved in many important real life applications such as biomedical image processing, remote sensing, wood species recognition, etc. Such situation has encouraged extensive researches to be conducted on texture, such as texture analysis and texture classification under the computer vision field. This paper has conducted a research study on texture classification, by using Discrete Wavelet Transform and Local Binary Pattern with Naïve Bayes as the main feature extraction and classification method respectively. The objective of this work is to discover the main factors that will affect the performance of discrete wavelet transform and LBP during a texture classification process. The experimental results show that the developed texture classific...
Performance Analysis of Texture Image Classification Using Wavelet Feature
International Journal of Image, Graphics and Signal Processing (IJIGSP) ISSN: 2074-9074(Print), ISSN: 2074-9082 (Online) Publisher: MECS, 2012
This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naïve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.
A Gray Texture Classification Using Wavelet and Curvelet Coefficients
Research Journal of Applied Sciences, Engineering and Technology, 2014
This study presents a framework for gray texture classification based on wavelet and curvelet features. The two main frequency domain transformations Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT) are analyzed. The features are extracted from the DWT and DCT decomposed image separately and their performances are evaluated independently. The performance metric used to analyze the system is classification accuracy. The standard benchmark database, Brodatz texture images are used for this study. The results show that, the curvelet based features provides better accuracy than wavelet based features.
Wavelet based texture classification
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000
Textures are one of the basic features in visual searching and computational vision. In the literature, most of the attention has been focussed on the texture features with minimal consideration of the noise models. In this paper we investigated the problem of texture classification from a maximum likelihood perspective. We took into account the texture model, the noise distribution, and the interdependence of the texture features. Our investigation showed that the real noise distribution is closer to an Exponential than a Gaussian distribution, and that the L 1 metric has a better retrieval rate than L 2. We also proposed the Cauchy metric as an alternative for both the L 1 and L 2 metrics. Furthermore, we provided a direct method for deriving an optimal distortion measure from the real noise distribution, which experimentally provides consistently improved results over the other metrics. We conclude with results and discussions on an international texture database.
Texture images: analysis, classification and comparison study
Texture is an important characteristic for image analysis. This paper contains study and review of most major image texture classification approaches using different wavelet domain feature extraction algorithms, which have been proposed in the recent literature for image-texture classification, and perform a comparative study. The objective of study is to offer a comparison study to help other researchers to find technique or combination of techniques to reduce complexity, while increasing the accuracy at the same time.
This paper provides a comparative study between the three approaches namely Wavelet transform, Local Binary Pattern and Dominant Local Binary Pattern to extract image features for texture classification .The dominant local binary pattern method makes use of the frequently occurred patterns to capture descriptive textural information. This performance of this method is compared by using the classification rate by conducting experiments on the broadtz tests.
An optimum feature extraction method for texture classification
Expert Systems With Applications, 2009
Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced. It used a combination of genetic algorithm, discrete wavelet transform and neural network for optimum feature extraction from texture images. An algorithm called the intelligent system, which processes the pattern recognition approximation, is developed. We tested the proposed method with several texture images. The overall success rate is about 95%.