Sparse Coding with Structured Sparsity Priors and Multilayer Architecture for Image Classification

Applying sparse coding on large dataset for image classification is a long standing problem in the field of computer vision. It has been found that the sparse coding models exhibit disappointing performance on these large datasets where variability is broad and anomalies are common. Conversely, deep neural networks thrive on bountiful data. Their success has encouraged researchers to try and augment the learning capacity of traditionally shallow sparse coding methods by adding layers. Multilayer sparse coding networks are expected to combine the best of both sparsity regularizations and deep architectures. To date, however, endeavors to marry the two techniques have not achieved significant improvements over their individual counterparts. In this thesis, we first briefly review multiple structured sparsity priors as well as various supervised dictionary learning techniques with applications on hyperspectral image classification. Based on the structured sparsity priors and dictionary learning techniques, we then develop a novel multilayer sparse coding network that contains thirteen sparse coding layers. The proposed sparse coding network learns both the dictionaries and the regularization parameters simultaneously using an end-to-end supervised learning scheme. We show empirical evidence that the regularization parameters can adapt to the given training data. We also propose applying dimension reduction within sparse coding networks to dramatically reduce the output dimensionality of the sparse coding layers and mitigate computational costs. Moreover, our sparse coding network is compatible with other powerful deep learning techniques such as drop out, batch normalization and shortcut connections. Experimental results show that the proposed multilayer sparse coding network produces classification accuracy competitive with the deep neural networks while using significantly fewer parameters and layers

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201