Light field technology has increasingly attracted the attention of the research community with its many possible applications. The lenslet array in commercial plenoptic cameras helps capture both the spatial and angular information of light rays in a single exposure. While the resulting high dimensionality of light field data enables its superior capabilities, it also impedes its extensive adoption. Hence, there is a compelling need for efficient compression of light field images. Existing solutions are commonly composed of several separate modules, some of which may not have been designed for the specific structure and quality of light field data. This increases the complexity of the codec and results in impractical decoding runtimes. We propose a new learning-based, disparity-aided model for compression of 4D light field images capable of parallel decoding. The model is endto-end trainable, eliminating the need for hand-tuning separate modules and allowing joint learning of rate and distortion. The disparity-aided approach ensures the structural integrity of the reconstructed light fields. Comparisons with the state of the art show encouraging performance in terms of PSNR and MS-SSIM metrics. Also, there is a notable gain in the encoding and decoding runtimes. Source code is available at https://moha23.github.io/LFDAAE.

Modeling image sets or videos as linear subspaces is quite popular for classification problems in machine learning. However, affine subspace modeling has not been explored much. In this article, we address the image sets classification problem by modeling them as affine subspaces. Affine subspaces are linear subspaces shifted from origin by an offset. The collection of the same dimensional affine subspaces of [Formula: see text] is known as affine Grassmann manifold (AGM) or affine Grassmannian that is a smooth and noncompact manifold. The non-Euclidean geometry of AGM and the nonunique representation of an affine subspace in AGM make the classification task in AGM difficult. In this article, we propose a novel affine subspace-based kernel that maps the points in AGM to a finite-dimensional Hilbert space. For this, we embed the AGM in a higher dimensional Grassmann manifold (GM) by embedding the offset vector in the Stiefel coordinates. The projection distance between two points in AGM is the measure of similarity obtained by the kernel function. The obtained kernel-gram matrix is further diagonalized to generate low-dimensional features in the Euclidean space corresponding to the points in AGM. Distance-preserving constraint along with sparsity constraint is used for minimum residual error classification by keeping the locally Euclidean structure of AGM in mind. Experimentation performed over four data sets for gait, object, hand, and body gesture recognition shows promising results compared with state-of-the-art techniques.

A video tensor is an organized multidimensional array of numerical values. In this paper, we explore the underlying manifold geometry of a video tensor by factorizing it using modified higher order singular value decomposition (HOSVD). Each factor (mode matrix) of a video tensor obtained after modified HOSVD can be thought of as a subspace and hence represents a point in Grassmann manifold (ℳGM). These factors cumulatively represent a point in product Grassmann manifold (ℳPGM). We propose a novel kernel for ℳPGMthat measures the similarity between two points in ℳPGMand generates a kernel-gram matrix. For representation learning, we diagonalize the obtained kernel-gram matrix and generate a small fixed length representation corresponding to each point in ℳPGM. Classification is performed in sparse framework with minimum residual error as classifier. Experimentation is carried out over Cambridge hand gesture and UMD Keck body gesture databases for both static and dynamic settings. Experimental study shows that even with small length feature representation, there is a significant improvement in classification results as compared to state-of the-art techniques.

AlexNet, one of the earliest and successful deep learning networks, has given great performance in image classification task. There are some fundamental properties for good classification such as: the network preserves the important information of the input data; the network is able to see differently, points from different classes. In this work we experimentally verify that these core properties are followed by the AlexNet architecture. We analyze the effect of linear and nonlinear transformations on input data across the layers. The convolution filters are modeled as linear transformations. The verified results motivate to draw conclusions on the desirable properties of transformation matrix that aid in better classification.

In this paper, we address the task of scene image classification in sparse framework. Recent scene image datasets consist of thousands of different size images with size of the order of 106pixels. Motivated by the fact that every image has a different size, we propose a dynamic kernel1which works over set of feature maps obtained for an image from last convolutional pooling layer of a pre-trained CNN. The size of feature maps depends on the input image size leading to the requirement of a dynamic kernel to compute similarity score between feature maps of different images. The kernel matrix obtained by using a dynamic kernel is large in size owing to the large number of training examples. To handle this we propose to use the concept of reduced virtual features (RVFs) obtained by diagonalizing the kernel matrix. RVF is a fixed length representation of a scene image irrespective of its true size. Classification is done in sparse framework by applying block sparsity constraint over sparse coefficients using dictionary built from RVFs. The proposed approach tested over standard datasets like Vogel-Schiele, MIT-8, MIT-67 and SUN-397 yields good results.

We propose a radiance domain denoising frame work for the high dynamic range (HDR) imaging problem. The proposed method uses a maximum aposteriori probability (MAP) based reconstruction of the HDR image with total variation (TV) as the prior to avoid unnecessary smoothing of the radiance field. To make the computation with TV prior efficient, we extend the majorize-minimize method of upper bounding the total variation by a quadratic function to our case which has a nonlinear term arising from the camera response function. A theoretical justification for doing radiance domain denoising as opposed to image domain denoising is also provided. Our method yields better results, with the edges well preserved and noise reduced considerably.