Modeling and Classification of Audio Signals Using Gradient-Based Fuzzy C-Means Algorithm with a Mercer Kernel
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Kernel (algebra)
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This paper introduces the theory of support vector machine (SVM) and applies it in image classification.Several kinds of visual features are extracted and used as input training vectors for SVM. Then the classification capability of single visual feature and combined feature are compared.The classi- fication effect of Polynomial kernel function is also contracted with Gaussian RBF kernel function.The results show that the combined feature is more discriminative than the single visual feature,and Gaus- sian RBF is better than Polynomial kernel function in the field of image classification.
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Kernel (algebra)
Feature (linguistics)
Feature vector
Contextual image classification
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Kernel (algebra)
Feature vector
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A novel method for anomaly detection in crowded scenes is presented. In our method, a new feature which named Mixture of Kernel Dynamic Texture was used for video representation. The MKDT method jointly models the appearance and dynamics of the scene. Based on this method, the abnormal detection includes temporal detection and spatial detection. The model for normal crowd behavior is based on MKDTs and outliers under this model are labeled as anomalies detection. Temporal anomalies are the events with low probability under the MKDT models. While spatial detection based on discriminant saliency is used to get a spatial detection map. The proposed representation is shown to outperform various state of the art abnormal detection methods.
Kernel (algebra)
Kernel density estimation
Representation
Feature (linguistics)
Foreground detection
Texture (cosmology)
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Scale-invariant feature transform
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Feature (linguistics)
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This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of features, and then a global Gaussian Mixture Model (GMM) learned from all images is used to randomly distribute each feature into one Gaussian component by a multinomial trial. The parameters of the multinomial distribution are defined by the posteriors of the feature on all the Gaussian components. Finally, the normalized means of the features distributed in every Gaussian component are concatenated to form a supervector, which is a compact representation for each scene image. We prove that these super-vectors observe the standard normal distribution. Our experiments on scene categorization tasks using this vector representation show significantly improved performance compared with the bag-of-features representation.
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Multinomial distribution
Feature vector
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This paper proposes a thenar palmprint classification method based on Support Vector Machine(SVM).It uses high-frequency emphasis filter to enhance the thenar palmprint image.Eight textural features which come from four directions are extracted as classification feature vectors.It compares the accuracy rate of classification in different kernel function,results show that the kernel-based SVM method which use combined feature vectors can give the best performance.
Kernel (algebra)
Feature (linguistics)
Feature vector
Local Binary Patterns
Binary classification
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Gaussian mixture model (GMM) is an efficient method for parametric clustering. However, traditional GMM can't perform clustering well on data set with complex structure such as images. In this paper, kernel trick, successfully used by SVM and kernel PCA, is introduced into EM algorithm for solving parameter estimation of GMM, which is so called kernel GMM (kGMM). The basic idea of kernel GMM is to apply kernel based GMM in feature space instead of in input data space. In order to avoid the curse of dimension, the proposed kGMM also embeds a step to automatically select discriminative features in feature space. kGMM is employed for the task of image binarization. Result shows that the proposed approach is feasible.
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The kernel and its parameters in support vector machine are important, an effect method for determining the parameter of Gaussian kernel based on the distances among the support vectors is proposed. The characters that the optimal discriminative function is determined by the support vectors, and the support vectors are centered as the Gaussian function, are considered in the method. Experimental results show that the method exhibits the essence of image feature space and solves a difficult problem for the parameter of Gaussian kernel in application.
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Discriminative model
Feature vector
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This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.
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Mean-shift
Similarity (geometry)
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