An Efficient Method for Texture Feature Extraction and Recognition based on Contourlet Transform and Canonical Correlation Analysis
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Abstract:
Feature extraction is an important processing step in texture classification. For feature extraction in contourlet domain, statistical features for blocks of subband are computed. In this paper, we present an efficient feature vector extraction method for texture classification. For more discriminative feature a canonical correlation analysis method is propose for feature vector fused to the different sample of texture in the same cluster. The KNN (K-Nearest Neighbor) classifier is utilizing to perform texture classification.
Keywords:
Contourlet
Discriminative model
Canonical correlation
Feature vector
Feature (linguistics)
Texture (cosmology)
This paper presents a novel approach for multi-feature information fusion. The proposed method is based on the Discriminative Multiple Canonical Correlation Analysis (DMCCA), which can extract more discriminative characteristics for recognition from multi-feature information representation. It represents the different patterns among multiple subsets of features identified by minimizing the Frobenius norm. We will demonstrate that the Canonical Correlation Analysis (CCA), the Multiple Canonical Correlation Analysis (MCCA), and the Discriminative Canonical Correlation Analysis (DCCA) are special cases of the DMCCA. The effectiveness of the DMCCA is demonstrated through experimentation in speaker recognition and speech-based emotion recognition. Experimental results show that the proposed approach outperforms the traditional methods of serial fusion, CCA, MCCA and DCCA.
Canonical correlation
Discriminative model
Feature (linguistics)
Representation
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Contourlet is a new effective signal representation tool in many imageapplications. Proposed is a contourlet-based image denoising algorithm using directional windows which takes advantage of the captured directional information of the images. Experiments show that the proposed algorithm achieves better performance than other contourlet-based image denoising algorithms.
Contourlet
Representation
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In the contourlet transform,the image obtained by Laplacian Pyramid decomposition may produce artifacts on singularity of signal,which is harmful to image denoising.Due to the lack,the improved contourlet transform which is composed of the improved Laplacian Pyramid(LP) decomposition is proposed,and the improved Laplacian Pyramid can effectively suppress the artifacts around the edge of the subband image obtained by contourlet transform.At the same time,SAR image enhancement algorithm based on improved contourlet transform is presented.Experiment results show that the algorithm is superior not only in speckle reduction but also in edge preservation.
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Speckle noise
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The contourlet Transform is an efficient directional multiresolution image representation, but it is not shift-invariant. The nonsubsampled contourlet transform (NSCT), is a fully shift-invariant, multiscale, and multidirection expansion that has a fast implementation, but the computational efficiency is lower than the contourlet transform, such an efficient representation has to be obtained by structured transform and fast algorithm. In this paper, we adopt an optimized directional filter bank and embed it into NSCT to pursue the desired speed, sacrificed the PSNR of the reconstructed image, we obtain the obvious increase of processing speed. Experimental results show that the quality of reconstructed image is sufficient for the human visual system (HVS), and the modified NSCT has a speed about several times than the speed of the customary one, this is very valuable for practical applications of the NSCT.
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Representation
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A novel hierarchical k-nearest neighbor classification method using the feature and observation space information is proposed. The present method performs a fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided.
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Feature (linguistics)
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fMPE is a previously introduced form of discriminative training, in which offsets to the features are obtained by training a projection from a high-dimensional feature space based on posteriors of Gaussians. This paper presents recent improvements to fMPE, including improved high-dimensional features which are easier to compute, and improvements to the training procedure. Other issues investigated include cross-testing of fMPE transforms (i.e. using acoustic models other than those with which the fMPE was trained) and the best way to train the Gaussians used to obtain the vector of posteriors.
Discriminative model
Feature vector
Feature (linguistics)
Training set
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Contourlet
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Contourlet transform overcomes the weakness of wavelet in higher dimensions. According to the theory of Contourlet, Contourlet can represent the characteristics of image. When Contourlet is applied to image fusion, the characteristic of original images can be effectively extracted and more important information is preserved. The Contourlet coefficients are recognized as independent in traditional image fusion method based on Contourlet. However,the coefficients of Contourlet have strongly dependency among different region and different direction subbands,and using the characteristic of Contourlet coefficients can design fusion rule. Experimental results have evidenced the effectiveness of the proposed method and it can preserve and extract the characteristic more reliable, accuracy and effective.
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In this paper, it presents a novel approach for selecting discriminative features in multimodal information fusion based discriminative multiple canonical correlation analysis (DMCCA), which is the generalized form of canonical correlation analysis (CCA), multiple canonical correlation analysis (MCCA) and discriminative canonical correlation analysis (DCCA). The proposed approach identifies the discriminative features from the multi-feature in Fractional Fourier Transform (FRFT) domain, which are capable of simultaneously maximizing the within-class correlation and minimizing the between-class correlation, leading to better utilization of the multi-feature information and producing more effective pattern recognition results. The effectiveness of the introduced solution is demonstrated through extensive experimentation on a visual based emotion recognition problem.
Discriminative model
Canonical correlation
Feature (linguistics)
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