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We here combine the rich, overcomplete signal representation afforded by the scattering transform together with a probabilistic graphical model which captures hierarchical dependencies between coefficients at different layers. The wavelet scattering network result in a high-dimensional representation which is translation invariant and stable to deformations whilst preserving informative content. Such...
Commonly, HoG/SVM classifier uses rectangular images for HoG feature descriptor extraction and training. This means significant additional work has to be done to process irrelevant pixels belonging to the background surrounding the object of interest. While some objects may indeed be square or rectangular, most of objects are not easily representable by simple geometric shapes. In Bitmap-HoG approach...
This paper deals with image categorization from weak supervision, e.g. global image labels. We propose to improve the region selection performed in latent variable models such as Latent Support Vector Machine (LSVM) by leveraging human eye movement features collected from an eye-tracker device. We introduce a new model, Gaze Latent Support Vector Machine (G-LSVM), whose region selection during training...
In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm...
Hierarchical classification is a computational efficient approach for large-scale image classification. The main challenging issue of this approach is to deal with error propagation. Irrelevant branching decision made at a parent node cannot be corrected at its child nodes in traversing the tree for classification. This paper presents a novel approach to reduce branching error at a node by taking...
In this work, we investigate the problem of predicting gender from still images using human metrology. Since the values of the anthropometric measurements are difficult to be estimated accurately from state-of-the-art computer vision algorithms, ratios of anthropometric measurements were used as features. Additionally, since several measurements will not be available at test time in a real-life scenario,...
In this paper, a new multi-class classification method is proposed and evaluated in the problem of human action recognition in unconstrained environments. The proposed method exploits both the maximum margin property of multi-class Support Vector Machines and Linear Discriminant Analysis-based discrimination. Experiments indicate that by exploiting such discriminant information in a multi-class maximum...
Intra-frame prediction in the High Efficiency Video Coding (HEVC) standard can be empirically improved by applying sets of recursive two-dimensional filters to the predicted values. However, this approach does not allow (or complicates significantly) the parallel computation of pixel predictions. In this work we analyze why the recursive filters are effective, and use the results to derive sets of...
We present a histogram-based real-time solution to detecting directly irradiated regions in digital fluoroscopic images. Our method leverages the power of model matching, machine learning and domain knowledge to characterize and segment images using histograms. The input image is automatically identified as containing partial, all, or null direct radiation. The regions with direct radiation are segmented...
Automatic diagnosis for fetal echocardiography plays an important part in diagnostic aid in the discrimination of congenital heart disease (CHD). Instead of traditional methods analyzing 2D cardiac echo video that need to find the standard view for discrimination, in this paper, we proposed a new system for automatic discrimination of CHD applying 4D original echocardiogram, which avoids the challenging...
Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification. However, the way in which information and invariance properties are encoded through in deep CNN architectures is still an open question. In this paper, we propose to modify the standard convolutional block of CNN in order to transfer more information layer after layer while keeping some invariance...
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