Learn More
The objective of this paper is classifying images by the object categories they contain, for example motorbikes or dolphins. There are three areas of novelty. First, we introduce a descriptor that represents local image shape and its spatial layout, together with a spatial pyramid kernel. These are designed so that the shape correspondence between two(More)
We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i) shape and appearance representations that support spatial pyramid matching over a region of interest. This generalizes the representation of Lazebnik et al., (2006) from an image(More)
We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of(More)
Given a set of images of scenes containing multiple object categories (e.g. grass, roads, buildings) our objective is to discover these objects in each image in an unsupervised manner, and to use this object distribution to perform scene classification. We achieve this discovery using probabilistic Latent Semantic Analysis (pLSA), a generative model from(More)
BACKGROUND The use of emergency hospital services (EHS) has increased steadily in Spain in the last decade while the number of immigrants has increased dramatically. Studies show that immigrants use EHS differently than native-born individuals, and this work investigates demographics, diagnoses and utilization rates of EHS in Lleida (Spain). METHODS(More)
We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic(More)
A scene description and segmentation system capable of recognising natural objects (e.g. sky, trees, grass) under different outdoor conditions is presented. We propose an hybrid and probabilistic classifier of image regions as a first step in solving the problem of scene context generation. We focus our work in the problem of image regions labeling to(More)
A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face(More)
Thousands of images are generated every day, which implies the necessity to classify, organise and access them using an easy, faster and efficient way. Scene classification, the classification of images into semantic categories (e.g. coast, mountains and streets), is a challenging and important problem nowadays. Many different approaches concerning scene(More)
A recent trend in digital mammography are CAD systems, which are computerized tools designed to help radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast is increased. In addition, the suitability of(More)