PolSAR image compression based on online sparse K-SVD dictionary learning

we present a novel polarimetric synthetic aperture radar (PolSAR) image compression scheme. PolSAR data contains lots of similar redundancies in single-channel and massively correlation between polarimetric channels. So these features make it difficult to represent PolSAR data efficiently. In this paper, discrete cosine transform (DCT) is adopted to remove redundancies between polarimetric channels, simple but quite efficient in improving compressibility. Sparse K-singular value decomposition (K-SVD) dictionary learning algorithm is utilized to remove redundancies within each channel image. Double sparsity scheme will be able to achieve fast convergence and low representation error by using a small number of sparsity dictionary elements, which is beneficial for the task of PolSAR image compression. Experimental results demonstrate that both numerical evaluation indicators and visual effect of reconstructed images outperform other methods, such as SPIHT, JPEG2000, and offline method.     

K-SVD with reference: an initialization method for dictionary learning

Recently, sparse signal recovery has received a lot attention for its wide real applications. Such a problem can be solved better if using a proper dictionary. Therefore, dictionary learning has become a promising direction and still been an open topic. As one of the greatest potential candidates, K-singular value decomposition (K-SVD) algorithm has been recognized by users. However, its performance has reached limitations of further improvement since it cannot consider the dependence between atoms. In this paper, we mine the inner structure of signals using their autocorrelations and make these prior as the reference. Based on these references, we present a new technique, which incorporates these references to K-SVD algorithm and provide a new method to initialize the dictionary. Experiments on synthetic data and image data show that the proposed algorithm has higher convergence ratio and lower error than the original K-SVD algorithm. Also, it performs better and more stable for sparse signal recovery.     

HEp-2 cell pattern classification with discriminative dictionary learning

The paper presents a supervised discriminative dictionary learning algorithm specially designed for classifying HEp-2 cell patterns. The proposed algorithm is an extension of the popular K-SVD algorithm: at the training phase, it takes into account the discriminative power of the dictionary atoms and reduces their intra-class reconstruction error during each update. Meanwhile, their inter-class reconstruction effect is also considered. Compared to the existing extension of K-SVD, the proposed algorithm is more robust to parameters and has better discriminative power for classifying HEp-2 cell patterns. Quantitative evaluation shows that the proposed algorithm outperforms general object classification algorithms significantly on standard HEp-2 cell patterns classifying benchmark¹ and also achieves competitive performance on standard natural image classification benchmark.     

Laplacian sparse dictionary learning for image classification based on sparse representation

Sparse representation is a mathematical model for data representation that has proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learning, and computer vision. As one of the building blocks of the sparse representation method, dictionary learning plays an important role in the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although using training samples directly as dictionary bases can achieve good performance, the main drawback of this method is that it may result in a very large and inefficient dictionary due to noisy training instances. To obtain a smaller and more representative dictionary, in this paper, we propose an approach called Laplacian sparse dictionary (LSD) learning. Our method is based on manifold learning and double sparsity. We incorporate the Laplacian weighted graph in the sparse representation model and impose the l₁-norm sparsity on the dictionary. An LSD is a sparse overcomplete dictionary that can preserve the intrinsic structure of the data and learn a smaller dictionary for each class. The learned LSD can be easily integrated into a classification framework based on sparse representation. We compare the proposed method with other methods using three benchmark-controlled face image databases, Extended Yale B, ORL, and AR, and one uncontrolled person image dataset, i-LIDS-MA. Results show the advantages of the proposed LSD algorithm over state-of-the-art sparse representation based classification methods.     

Elastic net regularized dictionary learning for image classification

Dictionary learning plays a key role in image representation for classification. A multi-modal dictionary is usually learned from feature samples across different classes and shared in the feature encoding process. Ideally each atom in dictionary corresponds to a single class of images, while each class of images corresponds to a certain group of atoms. Image features are encoded as linear combinations of selected atoms in a given dictionary. We propose to use elastic net as regularizer to select atoms in feature coding and related dictionary learning process, which not only benefits from the sparsity similar as ?₁ penalty but also encourages a grouping effect that helps improve image representation. Experimental results of image classification on benchmark datasets show that with dictionary learned in the proposed way outperforms state-of-the-art dictionary learning algorithms.     

Semi-supervised dictionary learning with label propagation for image classification

Computational Visual Media - Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is...     

类特定字典学习-局部核协同表示分类的人脸识别法

针对人脸识别中的光照变化和遮挡等固有难题,提出一种核函数和分块相结合的人脸识别方法。对人脸图像分块,利用高斯核映射各子块到更高维空间;结合类特定字典学习得到各子块对应的局部核协同表示的每类重构误差;根据重构误差的倒数以投票完成从人脸局部到全局的识别。在Extend Yale B,AR,CMU PIE人脸库上的实验表明:提出的方法不仅具有较高的识别正确率,同时对光照变化以及遮挡的人脸图像具有较强的鲁棒性。     

Efficient histogram dictionary learning for text/image modeling and classification

In dealing with text or image data, it is quite effective to represent them as histograms. In modeling histograms, although recent Bayesian topic models such as latent Dirichlet allocation and its variants are shown to be successful, they often suffer from computational overhead for inference of a large number of hidden variables. In this paper we consider a different modeling strategy of forming a dictionary of base histograms whose convex combination yields a histogram of observable text/image document. The dictionary entries are learned from data, which establishes direct/indirect association between specific topics/keywords and the base histograms. From a learned dictionary, the coding of an observed histogram can provide succinct and salient information useful for classification. One of our main contributions is that we propose a very efficient dictionary learning algorithm based on the recent Nesterov’s smooth optimization technique in conjunction with analytic solution methods for quadratic minimization sub-problems. Not alone the faster theoretical convergence rate, also in real time, our algorithm is 20–30 times faster than general-purpose optimizers such as interior-point methods. In classification/annotation tasks on several text/image datasets, our approach exhibits comparable or often superior performance to existing Bayesian models, while significantly faster than their variational inference.     

An efficient multiple-kernel learning for pattern classification

Support vector machines (SVMs) have been broadly applied to classification problems. However, a successful application of SVMs depends heavily on the determination of the right type and suitable hyperparameter settings of kernel functions. Recently, multiple-kernel learning (MKL) algorithms have been developed to deal with these issues by combining different kernels together. The weight with each kernel in the combination is obtained through learning. Lanckriet et al. proposed a way of deriving the weights by transforming the learning into a semidefinite programming (SDP) problem with a transduction setting. However, the amount of time and space required by this method is demanding. In this paper, we reformulate the SDP problem with an induction setting and incorporate two strategies to reduce the search complexity of the learning process, based on the comments discussed in the Lanckriet et al. paper. The primal and dual forms of SDP are derived. A discussion on computation complexity is given. Experimental results obtained from synthetic and benchmark datasets show that the proposed method runs efficiently in multiple-kernel learning.     

基于深度特征K-平均字典的场景识别

计算机视觉中的中级词袋模型广泛采用滑动窗口作为图片的分割方法。然而由滑动窗口产生的图块充满随机性,部分图块并没有明显的语义含义,会给后续的聚类带来困难。针对这个问题,提出采用似物检测取代滑动窗口。同时,根据词袋模型字典设计中关于字典词区别性和代表性的思路,对K-平均算法进行了改进,并在MIT-67室内场景数据库中进行了测试,该方法取得了良好的效果,最好的结果为76.31。     

基于对偶字典学习的图像超分辨技术研究

提出一种基于对偶字典学习的图像超分辨方法,通过稀疏重建的方法得到重建的图像,对偶字典通过稀疏表示将低分辨图像和高分辨图像联系起来.在稀疏表示过程中,低分辨图像在低分辨字典上的稀疏表示能够很好地提高对应的高分辨图像在高分辨字典上的稀疏表示效果.将字典的学习建模为包含l1范数优化问题的双层最优化问题,采用隐微分法计算随机梯度下降的期望梯度.仿真实验结果表明,该方法能够达到和联合字典学习方法相同的速度和质量,同时,在实际应用中可以通过神经网络模型学习方法提高算法的速度.与现有的算法比较,表明了该算法的有效性.     

Predictive high-level feature representation based on dictionary learning

A much improved computational performance of visual recognition tasks can be achieved by representing raw input data (low-level) with high-level feature representation. In order to generate the high-level representation, a sparse coding is widely used. However, a major problem in traditional sparse coding is computational performance due to an ℓ₀/ℓ₁ optimization. Often, this process takes significant amount of time to find the corresponding coding coefficients. This paper proposed a new method to create a discriminative sparse coding that is more efficient to compute the coding coefficients with minimum computational effort. More specifically, a linear model of sparse coding prediction was introduced to estimate the coding coefficients. This is accomplished by computing the matrix-vector product. We named this proposed method as predictive sparse coding K-SVD algorithm (PSC–KSVD). The experimental results demonstrated that PSC–KSVD achieved promising classification results on well-known benchmark image databases. Furthermore, it outperformed the currently approaches in terms of computational time. Consequently, PSC–KDVD can be considered as a suitable method to apply in real-time classification problems especially with large databases.     

An incremental learning vector quantization algorithm for pattern classification

Prototype classifiers have been studied for many years. However, few methods can realize incremental learning. On the other hand, most prototype classifiers need users to predetermine the number of prototypes; an improper prototype number might undermine the classification performance. To deal with these issues, in the paper we propose an online supervised algorithm named Incremental Learning Vector Quantization (ILVQ) for classification tasks. The proposed method has three contributions. (1) By designing an insertion policy, ILVQ incrementally learns new prototypes, including both between-class incremental learning and within-class incremental learning. (2) By employing an adaptive threshold scheme, ILVQ automatically learns the number of prototypes needed for each class dynamically according to the distribution of training data. Therefore, unlike most current prototype classifiers, ILVQ needs no prior knowledge of the number of prototypes or their initial value. (3) A technique for removing useless prototypes is used to eliminate noise interrupted into the input data. Results of experiments show that the proposed ILVQ can accommodate the incremental data environment and provide good recognition performance and storage efficiency.     

Task-driven dictionary learning

Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.     

Subsampled terahertz data reconstruction based on spatio-temporal dictionary learning

In this paper, the problem of terahertz pulsed imaging and reconstruction is addressed. It is assumed that an incomplete (subsampled) three dimensional THz data set has been acquired and the aim is to recover all missing samples. A sparsity-inducing approach is proposed for this purpose. First, a simple interpolation is applied to incomplete noisy data. Then, we propose a spatio-temporal dictionary learning method to obtain an appropriate sparse representation of data based on a joint sparse recovery algorithm. Then, using the sparse coefficients and the learned dictionary, the 3D data is effectively denoised by minimizing a simple cost function. We consider two types of terahertz data to evaluate the performance of the proposed approach: THz data acquired for a model sample with clear layered structures (e.g., a T-shape plastic sheet buried in a polythene pellet), and pharmaceutical tablet data (with low spatial resolution). The achieved signal-to-noise-ratio for reconstruction of T-shape data, from only 5% observation was 19 dB. Moreover, the accuracies of obtained thickness and depth measurements for pharmaceutical tablet data after reconstruction from 10% observation were 98.8%, and 99.9%, respectively. These results, along with chemical mapping analysis, presented at the end of this paper, confirm the accuracy of the proposed method.     

Multi-metric learning for multi-sensor fusion based classification

In this paper, we propose a multiple-metric learning algorithm to learn jointly a set of optimal homogenous/heterogeneous metrics in order to fuse the data collected from multiple sensors for joint classification. The learned metrics have the potential to perform better than the conventional Euclidean metric for classification. Moreover, in the case of heterogenous sensors, the learned multiple metrics can be quite different, which are adapted to each type of sensor. By learning the multiple metrics jointly within a single unified optimization framework, we can learn better metrics to fuse the multi-sensor data for a joint classification. Furthermore, we also exploit multi-metric learning in a kernel induced feature space to capture the non-linearity in the original feature space via kernel mapping.     

Ensemble dictionary learning for saliency detection

The human visual system (HSV) is quite adept at swiftly detecting objects of interest in complex visual scene. Simulating human visual system to detect visually salient regions of an image has been one of the active topics in computer vision. Inspired by random sampling based bagging ensemble learning method, an ensemble dictionary learning (EDL) framework for saliency detection is proposed in this paper. Instead of learning a universal dictionary requiring a large number of training samples to be collected from natural images, multiple over-complete dictionaries are independently learned with a small portion of randomly selected samples from the input image itself, resulting in more flexible multiple sparse representations for each of the image patches. To boost the distinctness of salient patch from background region, we present a reconstruction residual based method for dictionary atom reduction. Meanwhile, with the obtained multiple probabilistic saliency responses for each of the patches, the combination of them is finally carried out from the probabilistic perspective to achieve better predictive performance on saliency region. Experimental results on several open test datasets and some natural images demonstrate that the proposed EDL for saliency detection is much more competitive compared with some existing state-of-the-art algorithms.     

Subspace-level dictionary fusion for robust multimedia classification

Multimedia Tools and Applications - Nowadays, dictionary learning has become an important tool in many classification tasks, especially for images. The tailor-made atoms in a dictionary are trained...