Kernel Collaborative Representation Classification Research Articles

Diversity-Driven Multikernel Collaborative Representation Ensemble for Hyperspectral Image Classification

Recently, kernel collaborative representation classification (KCRC) has shown its outstanding performance in dealing with the problem of linear inseparability in hyperspectral remote sensing image classification. Meanwhile, ensemble learning has attracted great attention in improving the performance of a single classifier. Aiming at the limitation of a single classifier, bagging algorithm based on KCRC(KCRC-bagging) is presented in this article. The KCRC-bagging method uses bootstrap to increase the diversity of base classifiers, thus improving the classification accuracy and generalization performance. In order to reduce the scale of ensemble, a diversity-driven multikernel collaborative representation classifier ensemble approach (DIV-KCRC) is proposed. DIV-KCRC verifies the effectiveness of the representation classifier with the pair of diversity measures, and classifiers with high accuracy and diversity are selected to improve the classification performance and efficiency of the ensemble system. Three real hyperspectral data sets were applied to prove the validity of the proposed method. The experimental results demonstrate that both KCRC-bagging and DIV-KCRC can yield better classification performance than their corresponding base classifiers. In particular, DIV-KCRC provides more reliable classification results than KCRC-bagging.

Cross-view kernel collaborative representation classification for person re-identification

Currently, person re-identification (re-ID) has been applied in many public security applications. Yet owing to the big visual appearance changes of the same identity under different views, re-ID still faces many challenges. To reduce the intra-person discrepancy, extracting more power feature representations from pedestrian images is a reasonable solution. We propose a cross-view kernel collaborative representation based classification (CV-KCRC) method for person re-ID in our work. Our method aims to find more robust and discriminative feature representations that embody cross-view information to enhance the identification capability of features. We map the image features into a high dimensional feature space first and then use view-specific projection matrices to project the high dimensional features into a common low dimensional subspace. We expect that in the shared subspace the codings of same person from different views have the highest similarity and better performance can be achieved. Experiments on seven commonly used datasets reveal that our algorithm outperforms many state-of-the-art algorithms.

Fusing Deep Features by Kernel Collaborative Representation for Remote Sensing Scene Classification

Remote sensing scene classification is widely concerned because of its wide applications. Recently, convolutional neural networks (CNNs) have made a significant breakthrough in remote sensing image scene classification. However, the accuracy of using only fully connected layer of CNNs as a classifier is not satisfied, especially for few-shot remote sensing images. In this paper, we propose a framework of Feature-Fusion based Kernel Collaborative Representation Classification (FF-KCRC) for few-shot remote sensing images, which can make full use of the synergy between samples and the similarity between different types of image features to improve the accuracy of scene classification for few-shot remote sensing images. Specifically, we first design an effective feature extraction strategy to obtain more discriminative image features from CNNs, in which transfer learning is used to transfer the weights of pre-trained CNNs to alleviate the few-shot training problem. In the next, we design the FF-KCRC framework to make full use of the synergy between different categories and fuse the classification of different features, where kernel trick is used to address the problem of the linearly indivisibility. Extensive experiments have been conducted on publicly available remote sensing image data sets and the results show that the proposed FF-KCRC achieves stateof-the-art results.

Learning Multiple Parameters for Kernel Collaborative Representation Classification.

In this article, the problem of automatically learning multiple parameters for kernel collaborative representation classification (KCRC) is considered. We investigate the KCRC and measure its generalization error via leave-one-out cross-validation (LOO-CV). By taking advantage of the specific properties of KCRC, a closed-form expression is derived for the outputs of LOO-CV. Then, a simple classification rule that provides probabilistic outputs is adopted, and thereby, an effective loss function that is an explicit function with respect to the parameters is proposed as the generalization error. The gradients of the loss function are calculated, and the parameters are learned by minimizing the loss function using a gradient-based optimization algorithm. Furthermore, the proposed approach makes it possible to solve the multiple kernel/feature learning problems of KCRC effectively. Experiment results on six data sets taken from different scenes demonstrate the effectiveness of the proposed approach.

Hyperspectral Image Classification Using Kernel Fused Representation via a Spatial-Spectral Composite Kernel With Ideal Regularization

To adequately exploit spectral, spatial, and label information of the given hyperspectral data, a kernel fused representation-based classifier via a spatial-spectral composite kernel with ideal regularization (CKIR) method is proposed in this letter. Specifically, the learned CKIR is embedded into the kernel version of representation-based classifiers, i.e., kernel sparse representation-based classifier (KSRC) and kernel collaborative representation-based classifier (KCRC), to obtain more discriminative representation coefficients. Furthermore, to benefit from both sparsity and data correlation in representation, KSRC and KCRC are combined in the CKIR-based residual domain to further enhance the discriminative ability of the proposed classifier. The experimental results on two real hyperspectral images demonstrate that the proposed method outperforms the other state-of-the-art classifiers.

Hyperspectral and LiDAR Data Classification Using Kernel Collaborative Representation Based Residual Fusion

A new framework is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), local binary pattern (LBP), and kernel collaborative representation classification. Specifically, EP and LBP features are extracted from both sources. Then, the derived features of each source are classified by collaborative representation-based classifier with Tikhonov regularization (KCRT). Reconstruction residuals are fused to produce the final label assignment. Experimental results demonstrate that the proposed method outperforms the existing methods in hyperspectral and LiDAR data fusion.

Multiple kernel locality-constrained collaborative representation-based discriminant projection for face recognition

Abstract Collaborative representation-based classifier (CRC) has achieved superior classification performance in the field of face recognition. However, the performance of CRC will degrade significantly when facing non-linear structural data. To address this problem, many kernel CRC (KCRC) methods have been proposed. These methods usually use a predetermined kernel function which is difficult to be selected. In addition, how to select appropriate parameters remains a challenging problem. Hence, multiple kernel technology (MKL) is applied on CRC, which called MK-CRC. However, it only considers the representation errors while ignoring the class label information in the training process. In this paper, we propose a multiple kernel locality-constrained collaborative representation-based classifier (MKLCRC) which is the multiple kernel extension of CRC and considers the local structures of data. Based on the classification rule of MKLCRC, we propose a dimensionality reduction (DR) method called multiple kernel locality-constrained collaborative representation-based discriminant projection (MKLCR-DP). The goal of MKLCR-DP is to learn a projection matrix and a set of kernel weights to generate a low-dimensional subspace where the between-class reconstruction errors are maximized and the within-class reconstruction errors are minimized. Thus MKLCRC can achieve better performance in this low-dimensional subspace. The proposed method can be efficiently optimized with the trace ratio optimization. Experiments on AR, extended Yale B, FERET, CMU PIE and LFW face databases demonstrate that our method outperforms related state-of-the-art algorithms.

Multikernel Adaptive Collaborative Representation for Hyperspectral Image Classification

To adequately represent the nonlinearities in the high-dimensional feature space for hyperspectral images (HSIs), we propose a multiple kernel collaborative representation-based classifier (CRC) in this paper. Extended morphological profiles are first extracted from the original HSIs, because they can efficiently capture the spatial and spectral information. In the proposed method, a novel multiple kernel learning (MKL) model is embedded into CRC. Multiple kernel patterns, e.g., Naive, Multimetric, and Multiscale are adopted for the optimal set of basic kernels, which are helpful to capture the useful information from different pixel distributions, kernel metric spaces, and kernel scales. To learn an optimal linear combination of the predefined basic kernels, we add an extra training stage to the typical CRC where kernel weights are jointly learned with the representation coefficients from the training samples by minimizing the representation error. Moreover, by considering different contributions of dictionary atoms, the adaptive representation strategy is applied to the MKL framework via a dissimilarity-weighted regularizer to obtain a more robust representation of test pixels in the fused kernel space. Experimental results on three real HSIs confirm that the proposed classifiers outperform the other state-of-the-art representation-based classifiers.

Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning

In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.

Region-Based Relaxed Multiple Kernel Collaborative Representation for Hyperspectral Image Classification

This paper presents a region-based relaxed multiple kernel collaborative representation method for the spatial-spectral classification of hyperspectral images. The proposed method consists of three steps. In the first step, a multiscale method achieved by extending a superpixel segmentation algorithm is designed to capture the spatial-spectral information of hyperspectral images. For each scale, a hyperspectral image can be segmented into several nonoverlapping spectrally similar regions that consist of some spatially adjacent pixels. In the second step, two criteria (i.e., the first two moments) are computed within the regions of each scale to generate the corresponding spatial features. In the final step, a relaxed multiple kernel technique is proposed to fuse the obtained spatial multiscale features and original spectral features in the framework of column generation kernel collaborative representation classification. Experimental results obtained from two real hyperspectral images demonstrate the effectiveness of the proposed method as compared with some popular spatial-spectral techniques.

Superpixel-guided multiscale kernel collaborative representation for hyperspectral image classification

ABSTRACTThis article presents a superpixel-guided multiscale kernel collaborative representation method for robust classification of hyperspectral images. This novel method first exploits the spatial multiscale information of hyperspectral images by extending a superpixel segmentation algorithm, and then proposes a spatial-spectral information fusion technique to encode the spatial multiscale similarities and the spectral similarities between the pixels in the framework of kernel collaborative representation classification. The advantages of it mainly consist in (1) avoiding choosing empirical parameters in the spatial feature extraction process of superpixels and (2) enhanced classification accuracy as compared to traditional spatial-spectral kernel techniques. Experimental results with two widely used hyperspectral images demonstrate the effectiveness of the proposed method.

Kernel collaborative representation based dictionary learning and discriminative projection

Sparse representation based classification (SRC) has been developed and shown great potential due to its effectiveness in various classification tasks. But how to determine appropriate features that can best work with SRC remains an open question. Based on SRC and dimensionality reduction (DR) techniques, a simultaneous discriminative projection and dictionary learning method (DSRC) is proposed. However, as a linear algorithm, DSRC cannot handle the data with highly nonlinear distribution. Recently research has shown that the collaborative representation mechanism is more important to the success of SRC. Motivated by these concerns, in this paper, we propose a novel kernel collaborative representation based classifier (KCRC), and then we use it as a criterion to design a kernel collaborative representation based dictionary learning and discriminant projection method (KDL-DP). The proposed method aims at learning a projection matrix and a dictionary such that in the low dimension subspace the between-class reconstruction residual of a given data set is maximized and the within-class reconstruction residual is minimized. Extensive experimental results validate the superiority of the proposed approach when compared with the state-of-the-art methods.

KCRC-LCD: Discriminative kernel collaborative representation with locality constrained dictionary for visual categorization

We consider the image classification problem via kernel collaborative representation classification with locality constrained dictionary (KCRC-LCD). Specifically, we propose a kernel collaborative representation classification (KCRC) approach in which kernel method is used to improve the discrimination ability of collaborative representation classification (CRC). We then measure the similarities between the query and atoms in the global dictionary in order to construct a locality constrained dictionary (LCD) for KCRC. In addition, we discuss several similarity measure approaches in LCD and further present a simple yet effective unified similarity measure whose superiority is validated in experiments. There are several appealing aspects associated with LCD. First, LCD can be nicely incorporated under the framework of KCRC. The LCD similarity measure can be kernelized under KCRC, which theoretically links CRC and LCD under the kernel method. Second, KCRC-LCD becomes more scalable to both the training set size and the feature dimension. Example shows that KCRC is able to perfectly classify data with certain distribution, while conventional CRC fails completely. Comprehensive experiments on widely used public datasets also show that KCRC-LCD is a robust discriminative classifier with both excellent performance and good scalability, being comparable or outperforming many other state-of-the-art approaches.