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

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1