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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...
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...
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...
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