Non-negativity and dependence constrained sparse coding for image classification

Non-negativity and locality constrained Laplacian sparse coding for image classification

Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.

Image representation using local features and sparse coding (SC) plays a very important role in image classification when the dataset is fairly large. Despite of its worldwide popularity, there are still some improving space in classification efficiency and computational investment in training and coding phrase of SC. In this paper, we put forward a novel object categories recognition method from two aspects. First, the contextual relevance between image patches are fully utilized by merging local feature of every sub-patch with its neighboring ones into strong context features to generate the multiple sparse representations, which are received by the SC and multi-scale max pooling SPM(Spatial Pyramid Matching), respectively. Second, while calculating the sparse coefficients of SC, we need to solve L1-regularized least square problem. Screening out the zero coefficients and discarding the corresponding inactive codewords before solving Lasso problem can remarkably speed up the optimization. The proposed method outperforms state-of-the-art performance in a large number of image categorization experiments on several benchmarks: the ground truth dataset (21 Land-Use database), the event dataset (UIUC-Sport dataset), and the object recognition dataset (Caltech101 dataset).

Image representation using local features and sparse coding (SC) plays a very important role in image classification when the dataset is fairly large. Despite of its worldwide popularity, there are still some improving space in classification efficiency and computational investment in training and coding phrase of SC. In this paper, we put forward a novel object categories recognition method from two aspects. First, the contextual relevance between image patches are fully utilized by merging local feature of every sub-patch with its neighboring ones into strong context features to generate the multiple sparse representations, which are received by the SC and multi-scale max pooling SPM(Spatial Pyramid Matching), respectively. Second, while calculating the sparse coefficients of SC, we need to solve L1-regularized least square problem. Screening out the zero coefficients and discarding the corresponding inactive codewords before solving Lasso problem can remarkably speed up the optimization. The proposed method outperforms state-of-the-art performancein a large number of image categorization experiments on several benchmarks: the ground truth dataset (21 Land-Use database), the event dataset (UIUC-Sport dataset), and the object recognition dataset (Caltech101 dataset).

Recently support vector machines (SVM) using spatial pyramid matching (SPM) kernel have been highly successful in image classification applications. And linear spatial pyramid matching using sparse coding (ScSPM) scheme has been proposed to enhance the performance of SPM both in time and classification accuracy. In order to reduce the time complexity of dictionary construction process, sparse coding with affinity propagation method has been proposed in this paper. Because the dictionary used for sparse coding plays a key role in these methods, we also adopt supervised dictionary learning method to construct dictionary. The coding coefficients of each class have greater separability for SVM classification. Substantial experiments on Scene15 and CalTech101 image datasets have been conducted to investigate the performance of proposed approach in multi-class image classification; the results show that the approach can reach higher accuracy compared with ScSPM.

Introducing a weighted non-negative matrix factorization for image classification

A selection of pooling region is an important issue on image classification process. Although Spatial Pyramid Matching (SPM) simply selects pooling regions by partitioning an image into rectangular grids, the SPM has been used the most until now to exploit the spatial information of features. In this paper, in addition to the SPM, we concatenate a novel pooling method which decomposes an image into an object region and a background region. Since the regions deal with not only the spatial information but also objectness that is higher level knowledge than the simple rectangular grids, flexible sub-regions can be achieved with our method. In order to estimate object region from an unlabeled image, we adopt the visual saliency map for a probabilistic measure of objectness. To do this, our soft region pooling replaces the existing max pooling, which helps features inside the probabilistic boundary of object/background region to be selected. Beyond applying the objectness score only to select features for the max pooling, weight for each element of image representation vector is also applied to emphasize an important component vector. The proposed method is implemented based on LLC, and tested on widely used dataset, Caltech-101 and Caltech-256. The proposed method achieves 2.77% improvement on Caltech-101 and achieves 1.84% improvement on Caltech-256 compared with the accuracy of LLC.

Image classification is an important problem in computer vision. The sparse coding spatial pyramid matching (ScSPM) framework is widely used in this field. However, the sparse coding cannot effectively handle very large training sets because of its high computational complexity, and ignoring the mutual dependence among local features results in highly variable sparse codes even for similar features. To overcome the shortcomings of previous sparse coding algorithm, we present an image classification method, which replaces the sparse dictionary with a stable dictionary learned via low computational complexity clustering, more specifically, a k-medoids cluster method optimized by k-means++. The proposed method can reduce the learning complexity and improve the feature’s stability. In the experiments, we compared the effectiveness of our method with the existing ScSPM method and its improved versions. We evaluated our approach on two diverse datasets: Caltech-101 and UIUC-Sports. The results show that our method can increase the accuracy of spatial pyramid matching, which suggests that our method is capable of improving performance of sparse coding features.

Fast low rank representation based spatial pyramid matching for image classification

Non-negative matrix factorization (NNMF) technique has been widely applicable for dimension reduction. NNMF has shown its usability to solve numerous challenges in areas such as signal processing, image classification, and text mining. Existing matrix factorization methods need a predefined value of rank of a base matrix. Our primary intention in this article is to automatically identify the optimum value of the rank to factorize the matrix. Here we propose an Incremental Cauchy NonNegative Matrix factorization (ICNNMF) which automatically finds the optimum rank and separates the overlapping sounds received from a single channel of a source. Classification is performed using an ensemble of one class fuzzy rule-based (FRB) classifiers. We evaluated the proposed method over the real data set, and the proposed model has shown better accuracy as compared to the traditional classifier. Moreover, the proposed matrix factorization method automatically determines the optimal value of the rank and adapt to new data.

Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization process of BoW based image representation. However, in the feature quantization process of sparse coding, some similar local features may be quantized into different visual words of the codebook due to the sensitiveness of quantization. In this paper, to alleviate the impact of this problem, we propose a Laplacian sparse coding method, which will exploit the dependence among the local features. Specifically, we propose to use histogram intersection based kNN method to construct a Laplacian matrix, which can well characterize the similarity of local features. In addition, we incorporate this Laplacian matrix into the objective function of sparse coding to preserve the consistence in sparse representation of similar local features. Comprehensive experimental results show that our method achieves or outperforms existing state-of-the-art results, and exhibits excellent performance on Scene 15 data set.

Image representation based on sparse coding generalizes the bag of words model. Although it reduces the reconstruction error for local features to achieve the state-of-the-art image classification performance, the large computational cost hinders the application of sparse coding-based image features. In this paper, we propose approximating a sparse code using the output of a simple neural network. The resulting parameter learning model for the neural network automatically incorporates non-negative and shift-invariant constraints, leading to an efficient normalized non-negative sparse coding (N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC) sparse encoder. Without the use of the traditional iterative process to solve the sparse coding objective, the sparse encoder directly “converts” each local feature into a sparse code. We also introduce a method for training the encoder based on the auto-encoder method. In addition, we formally propose the corresponding sparse coding scheme called N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC, which enforces both the non-negative constraint and the shift-invariant constraint in addition to the traditional sparse coding criteria. As demonstrated by several experiments, the obtained N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC encoder requires only 3%-10% of the processing time for image feature extraction compared with the standard sparse coding scheme. At the same time, the features extracted using the exact solutions of the N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC coding scheme and the N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> SC encoder offer superior image classification accuracy compared to the accuracy of many existing sparse coding-based representations.

Depth information is being widely used in many real-world applications. However, due to the limitation of depth sensing technology, the captured depth map in practice usually has much lower resolution than that of color image counterpart. In this paper, we propose to joint exploit the internal smoothness prior and external gradient consistency constraint in graph domain for depth super-resolution. On one hand, a new graph Laplacian regularizer is proposed to the preserve the inherent piecewise smooth characteristic of depth, which has desirable filtering properties. On the other hand, inspired by an observation that the gradient of depth is zero except at edge separating regions, we introduce a graph gradient consistency constraint to enforce that the graph gradient of depth is close to the thresholded gradient of guidance. Finally, the internal and external regularizations are casted into a unified optimization framework, which can be efficiently addressed by ADMM. Experiments results demonstrate that our method outperforms the state-of-the-art with respect to both objective and subjective quality evaluations.

Sparse coding has received an increasing amount of interest in recent years. It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set. Originally applied to modeling the human visual cortex, sparse coding has been shown useful for many applications. However, most of the existing approaches to sparse coding fail to consider the geometrical structure of the data space. In many real applications, the data is more likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. It has been shown that the geometrical information of the data is important for discrimination. In this paper, we propose a graph based algorithm, called graph regularized sparse coding, to learn the sparse representations that explicitly take into account the local manifold structure of the data. By using graph Laplacian as a smooth operator, the obtained sparse representations vary smoothly along the geodesics of the data manifold. The extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed algorithm.

Fast human action classification and VOI localization with enhanced sparse coding