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Mehrdad J. Gangeh - One of the best experts on this subject based on the ideXlab platform.

  • A Two-Stage Combined Classifier in Scale Space Texture Classification
    arXiv: Computer Vision and Pattern Recognition, 2012
    Co-Authors: Mehrdad J. Gangeh, Robert P. W. Duin, Bart M. Ter Haar Romeny, Mohamed S. Kamel
    Abstract:

    Textures often show multiscale properties and hence multiscale techniques are considered useful for texture analysis. Scale-space theory as a biologically motivated approach may be used to construct multiscale textures. In this paper various ways are studied to combine features on different scales for texture classification of small image patches. We use the N-jet of derivatives up to the second order at different scales to generate distinct pattern representations (DPR) of feature subsets. Each feature subset in the DPR is given to a base Classifier (BC) of a two-stage Combined Classifier. The decisions made by these BCs are Combined in two stages over scales and derivatives. Various combining systems and their significances and differences are discussed. The learning curves are used to evaluate the performances. We found for small sample sizes combining Classifiers performs significantly better than combining feature spaces (CFS). It is also shown that combining Classifiers performs better than the support vector machine on CFS in multiscale texture classification.

  • A Two-Stage Combined Classifier in Scale Space Texture Classification
    arXiv: Computer Vision and Pattern Recognition, 2012
    Co-Authors: Mehrdad J. Gangeh, Robert P. W. Duin, Bart M. Ter Haar Romeny, Mohamed S. Kamel
    Abstract:

    Textures often show multiscale properties and hence multiscale techniques are considered useful for texture analysis. Scale-space theory as a biologically motivated approach may be used to construct multiscale textures. In this paper various ways are studied to combine features on different scales for texture classification of small image patches. We use the N-jet of derivatives up to the second order at different scales to generate distinct pattern representations (DPR) of feature subsets. Each feature subset in the DPR is given to a base Classifier (BC) of a two-stage Combined Classifier. The decisions made by these BCs are Combined in two stages over scales and derivatives. Various combining systems and their significances and differences are discussed. The learning curves are used to evaluate the performances. We found for small sample sizes combining Classifiers performs significantly better than combining feature spaces (CFS). It is also shown that combining Classifiers performs better than the support vector machine on CFS in multiscale texture classification.

  • SCIA - Scale-space texture classification using Combined Classifiers
    Image Analysis, 2007
    Co-Authors: Mehrdad J. Gangeh, Bart M. Ter Haar Romeny, Chikkannan Eswaran
    Abstract:

    Since texture is scale dependent, multi-scale techniques are quite useful for texture classification. Scale-space theory introduces multi-scale differential operators. In this paper, the N-jet of derivatives up to the second order at different scales is calculated for the textures in Brodatz album to generate the textures in multiple scales. After some preprocessing and feature extraction using principal component analysis (PCA), instead of combining features obtained from different scales/derivatives to construct a Combined feature space, the features are fed into a two-stage Combined Classifier for classification. The learning curves are used to evaluate the performance of the proposed texture classification system. The results show that this new approach can significantly improve the performance of the classification especially for small training set size. Further, comparison between Combined feature space and Combined Classifiers shows the superiority of the latter in terms of performance and computation complexity.

  • Combined Classifier versus Combined Feature Space in Scale Space Texture Classification
    2007
    Co-Authors: Mehrdad J. Gangeh, B.m. Ter Haar Romenij, C. Eswaran
    Abstract:

    Using Combined Classifiers alleviates the problem of generating a large feature space, as the features generated from each scale/derivative are directly fed to a base Classifier. In this approach, instead of concatenating features generated from each scale/derivative, the decision made by the base Classifiers are Combined in a two-stage Combined Classifier.In this paper, the performance of the proposed classification system is first compared against the Combined feature space for only the zeroth order Gaussian derivative at multiple scales. The results clearly show that the proposed system using Combined Classifiers outperforms the classical approach of the Combined feature space. The significance of the parameters, especially the fraction of variance maintained after applying PCA (principal component analysis) is also discussed.

  • Scale Space Texture Classification Using Combined Classifiers with Application to Ultrasound Tissue Characterization
    3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 2007
    Co-Authors: Mehrdad J. Gangeh, C. Eswaran, Rpw Duin, Ter Bm Bart Haar Romeny
    Abstract:

    Texture is often considered as a repetitive pattern and the constructing structure is known as texel. The granularity of a texture, i.e. the size of a texel, is different from one texture to another and hence inspiring us applying scale space techniques to texture classification. In this paper Gaussian kernels with different variances (σ2) are convolved with the textures from Brodatz album to generate the textures in different scales. After some preprocessing and feature extraction using principal component analysis (PCA), the features are fed to a Combined Classifier for classification. The learning curves are used to evaluate the performance of the texture Classifier system designed. The results of classification show that the scale space texture classification approach used can significantly improve the performance of the classification especially for small training set size. This is very important in applications where the training set data is limited. The application of this method to ultrasound liver tissue characterization for discrimination of normal liver from cirrhosis yields promising results.

Ter Bm Bart Haar Romeny - One of the best experts on this subject based on the ideXlab platform.

  • Scale Space Texture Classification Using Combined Classifiers with Application to Ultrasound Tissue Characterization
    3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 2007
    Co-Authors: Mehrdad J. Gangeh, C. Eswaran, Rpw Duin, Ter Bm Bart Haar Romeny
    Abstract:

    Texture is often considered as a repetitive pattern and the constructing structure is known as texel. The granularity of a texture, i.e. the size of a texel, is different from one texture to another and hence inspiring us applying scale space techniques to texture classification. In this paper Gaussian kernels with different variances (σ2) are convolved with the textures from Brodatz album to generate the textures in different scales. After some preprocessing and feature extraction using principal component analysis (PCA), the features are fed to a Combined Classifier for classification. The learning curves are used to evaluate the performance of the texture Classifier system designed. The results of classification show that the scale space texture classification approach used can significantly improve the performance of the classification especially for small training set size. This is very important in applications where the training set data is limited. The application of this method to ultrasound liver tissue characterization for discrimination of normal liver from cirrhosis yields promising results.

C. Eswaran - One of the best experts on this subject based on the ideXlab platform.

  • Combined Classifier versus Combined Feature Space in Scale Space Texture Classification
    2007
    Co-Authors: Mehrdad J. Gangeh, B.m. Ter Haar Romenij, C. Eswaran
    Abstract:

    Using Combined Classifiers alleviates the problem of generating a large feature space, as the features generated from each scale/derivative are directly fed to a base Classifier. In this approach, instead of concatenating features generated from each scale/derivative, the decision made by the base Classifiers are Combined in a two-stage Combined Classifier.In this paper, the performance of the proposed classification system is first compared against the Combined feature space for only the zeroth order Gaussian derivative at multiple scales. The results clearly show that the proposed system using Combined Classifiers outperforms the classical approach of the Combined feature space. The significance of the parameters, especially the fraction of variance maintained after applying PCA (principal component analysis) is also discussed.

  • Scale Space Texture Classification Using Combined Classifiers with Application to Ultrasound Tissue Characterization
    3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 2007
    Co-Authors: Mehrdad J. Gangeh, C. Eswaran, Rpw Duin, Ter Bm Bart Haar Romeny
    Abstract:

    Texture is often considered as a repetitive pattern and the constructing structure is known as texel. The granularity of a texture, i.e. the size of a texel, is different from one texture to another and hence inspiring us applying scale space techniques to texture classification. In this paper Gaussian kernels with different variances (σ2) are convolved with the textures from Brodatz album to generate the textures in different scales. After some preprocessing and feature extraction using principal component analysis (PCA), the features are fed to a Combined Classifier for classification. The learning curves are used to evaluate the performance of the texture Classifier system designed. The results of classification show that the scale space texture classification approach used can significantly improve the performance of the classification especially for small training set size. This is very important in applications where the training set data is limited. The application of this method to ultrasound liver tissue characterization for discrimination of normal liver from cirrhosis yields promising results.

Rpw Duin - One of the best experts on this subject based on the ideXlab platform.

  • Scale Space Texture Classification Using Combined Classifiers with Application to Ultrasound Tissue Characterization
    3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 2007
    Co-Authors: Mehrdad J. Gangeh, C. Eswaran, Rpw Duin, Ter Bm Bart Haar Romeny
    Abstract:

    Texture is often considered as a repetitive pattern and the constructing structure is known as texel. The granularity of a texture, i.e. the size of a texel, is different from one texture to another and hence inspiring us applying scale space techniques to texture classification. In this paper Gaussian kernels with different variances (σ2) are convolved with the textures from Brodatz album to generate the textures in different scales. After some preprocessing and feature extraction using principal component analysis (PCA), the features are fed to a Combined Classifier for classification. The learning curves are used to evaluate the performance of the texture Classifier system designed. The results of classification show that the scale space texture classification approach used can significantly improve the performance of the classification especially for small training set size. This is very important in applications where the training set data is limited. The application of this method to ultrasound liver tissue characterization for discrimination of normal liver from cirrhosis yields promising results.

Zhong Gao - One of the best experts on this subject based on the ideXlab platform.

  • classification using a novel Combined Classifier for digital modulations in digital television communication
    Workshop on Knowledge Discovery and Data Mining, 2009
    Co-Authors: Zhong Gao
    Abstract:

    With the rapid development of the communication technology, the communication environment becomes more and more complicated these years. Many signal modulation types are used simultaneously in digital TV communication systems. Therefore, a need arises for modulation classification that can automatically detect the incoming modulation type. In this paper, we propose a new approach for modulation classification, which uses a novel Combined Classifier based on multi-class support vector machine (SVM) and fuzzy integral to make the classification more suitable and accurate for signals in a wide range of signal to noise rate (SNR). Further, three efficient features with high robustness and less computation are extracted from intercepted signals to classify eleven digital modulation types. The experimental results show that the proposed scheme has the advantages of high accuracy and reliability.

  • applying a novel Combined Classifier for pornographic web filtering in a grid computing environment
    Computer Supported Cooperative Work in Design, 2008
    Co-Authors: Zhong Gao, Xin Zhao, Danni Qin, Mei Qin
    Abstract:

    As the Web expands exponentially, there are a flood of pornographic Web sites on the Internet. Thus effective and fast web filtering systems are essential. Web filtering based on hypertext classification has become one of the important techniques to handle and filter inappropriate information on the Web. The task involved can be parallelized and distributed in a grid environment. However, how to improve the performance of the hypertext classification under the situation of noisy data is still a challenging problem. In this paper, we propose a new approach for hypertext classification in Web filtering, which uses a novel support vector machine and k-nearest neighbor (KNN-SVM) to remove noisy training examples. The task of text categorization is distributed in several computers. The experimental results show that the generalization performance in the accuracy of classification and the processing time are improved significantly compared to that of the traditional SVM Classifier over the grid, and adapt to engineering applications.

  • applying a novel Combined Classifier for hypertext classification in pornographic web filtering
    International Conference on Internet Computing for Science and Engineering, 2008
    Co-Authors: Zhong Gao, Hao Dong, Shutong Wang, Haibo Wang, Xiaopei Wei
    Abstract:

    As the Web expands exponentially, there are a flood of pornographic Web sites on the Internet. Thus effective Web filtering systems are essential. Web filtering based on hypertext classification has become one of the important techniques to handle and filter inappropriate information on the Web. Hypertext classification, that is the automatic classification of Web documents into predefined classes, came to elevate humans from that task. However, how to improve the performance of the hypertext classification under the situation of noisy data is still a challenging problem. In this paper, we propose a new approach for hypertext classification in Web filtering, which uses a novel support vector machine and K-nearest neighbor (KNN-SVM) to remove noisy training examples. The experimental results show that the generalization performance and the accuracy of classification are improved significantly compared to that of the traditional SVM Classifier, and adapt to engineering applications.