Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines

Despite the recent deep learning (DL) revolution, kernel machines still remain powerful methods for action recognition. DL has brought the use of large datasets and this is typically a problem for kernel approaches, which are not scaling up eciently due to kernel Gram matrices. Nevertheless, kernel methods are still attractive and more generally applicable since they can equally manage dierent sizes of the datasets, also in cases where DL techniques show some limitations. This work investigates these issues by proposing an explicit ap- proximated representation that, together with a linear model, is an equivalent, yet scalable, implementation of a kernel machine. Our approximation is directly inspired by the exact feature map that is induced by an RBF Gaussian kernel but, unlike the latter, it is nite dimensional and very compact. We justify the soundness of our idea with a theoretical analysis which proves the unbiasedness of the approximation, and provides a vanishing bound for its variance, which is shown to decrease much rapidly than in alternative methods in the literature. In a broad experimental validation, we assess the superiority of our approximation in terms of 1) ease and speed of training, 2) compactness of the model, and 3) improvements with respect to the state-of-the-art performance

Multipass SAR interferometry. A tool for geologic analysis

This paper investigates how the information content of repeat pass satellite SAR interferometric (INSAR) data can be used to provide the geologist with a tool which can improve his ability and efficacy in the geologic analysis of SAR imagery. INSAR processing produces interferometric fringes, coherence and amplitude images. To produce an interferometric DEM phase unwrapping is a critical step. For phase unwrapping, we propose the WLMS (Weighted Least Mean Square) estimation of the phase, which is a generalization of the least-mean square method. The crucial step in WLMS approach is the weighting procedure. We propose a weighting algorithm based on the fusion of a priori information extracted from different interferometric products. These different information channels—DEM, amplitude and coherence—can be effectively fused to convey information to the geologic interpreter using 3D stereoscopic visualization;SAR stereo pairs were artificially generated using the interferometric DEM and the intensity image or the coherence image of the area overlaid. In order to ascertain the performance of the procedure a number of tests were carried out over various sites in Matese (Southern Italy), which has a fairly demanding topography, using ERS SAR tandem data. The results demonstrate that WLMS unwrapping method is sufficiently robust in capturing the morphology of the area and that stereoscopic visualization greatly facilitates geologic interpretation and the observation of detailed features of the terrain

Intra-Camera Supervised Person Re-Identification

Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation process, leading to poor scalability in practical re-id applications. On the other hand unsupervised re-id methods do not need identity label information, but they usually suffer from much inferior and insufficient model performance. To overcome these fundamental limitations, we propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process, significantly reducing the amount of human annotation efforts. Consequently, it gives rise to a more scalable and more feasible setting, which we call Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for self-discovering the cross-camera identity correspondence in a per-camera multi-task inference framework. Extensive experiments demonstrate the cost-effectiveness superiority of our method over the alternative approaches on three large person re-id datasets. For example, MATE yields 88.7% rank-1 score on Market-1501 in the proposed ICS person re-id setting, significantly outperforming unsupervised learning models and closely approaching conventional fully supervised learning competitors

Classification of schizophrenia using feature-based morphometry

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Software for full-color 3D reconstruction of the biological tissues internal structure

A software for processing sets of full-color images of biological tissue histological sections is developed. We used histological sections obtained by the method of high-precision layer-by-layer grinding of frozen biological tissues. The software allows restoring the image of the tissue for an arbitrary cross-section of the tissue sample. Thus, our method is designed to create a full-color 3D reconstruction of the biological tissue structure. The resolution of 3D reconstruction is determined by the quality of the initial histological sections. The newly developed technology available to us provides a resolution of up to 5 - 10 {\mu}m in three dimensions.Comment: 11 pages, 8 figure

Combining free energy score spaces with information theoretic kernels: Application to scene classification

Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vectorial data (e.g., support vector machines) are learned discriminatively. A generative embed-ding is a mapping from the object space into a fixed dimensional score space, induced by a generative model, usually learned from data. The fixed dimensionality of these generative score spaces makes them adequate for discriminative learning of classifiers, thus bringing together the best of the discriminative and generative paradigms. In particular, it was recently shown that this hybrid ap-proach outperforms a classifier obtained directly for the generative model upon which the score space was built. Using a generative embedding involves two steps: (i) defining and learning the generative model and using it to build the embed-ding; (ii) discriminatively learning a (maybe kernel) classifier on the adopted score space. The literature on generative embeddings is es-sentially focused on step (i), usually using some standard off-the-shelf tool for step (ii). In this paper, we adopt a different approach, by focusing also on the discriminative learning step. In particular, we combine two very recent and top performing tools in each of the steps: (i) the free energy score space; (ii) non-extensive information theoretic kernels. In this paper, we apply this methodology in scene recognition. Experimental results on two benchmark datasets shows that our approach yields state-of-the-art performance. Index Terms — Scene categorization, generative embeddings, score spaces, information theoretic kernels

Heat diffusion based dissimilarity analysis for schizophrenia classification

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