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An SVM Confidence-Based Approach to Medical Image Annotation

Publications associées (39)

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Learning Image Descriptors with the Boosting-Trick

Pascal Fua, Vincent Lepetit, Tomasz Piotr Trzcinski, Christos Marios Christoudias

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively repre- sent a s ...

2012

LDAHash: Improved Matching with Smaller Descriptors

Pascal Fua, Christoph Strecha

SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to ...

Institute of Electrical and Electronics Engineers2012

On the Challenge of Classifying 52 Hand Movements from Surface Electromyography

Barbara Caputo, Ilja Kuzborskij

The level of dexterity of myoelectric hand prostheses depends to large extent on the feature representation and subsequent classification of surface electromyography signals. This work presents a comparison of various feature extraction and classification ...

2012

LDAHash: Improved matching with smaller descriptors

Pascal Fua, Christoph Strecha

SIFT-like local feature descriptors are ubiquitously employed in such computer vision applications as content-based retrieval, video analysis, copy detection, object recognition, photo-tourism and 3D reconstruction. Feature descriptors can be designed to b ...

2010

Improved Phone Posterior Estimation Through k-NN and MLP-Based Similarity

Benjamin Picart

In this work, we investigate the possible use of k-nearest neighbour (kNN) classifiers to perform frame-based acoustic phonetic classification, hence replacing Gaussian Mixture Models (GMM) or MultiLayer Perceptrons (MLP) used in standard Hidden Markov Mod ...

Idiap2009

Multi-layer Boosting for Pattern Recognition

François Fleuret

We extend the standard boosting procedure to train a two-layer classifier dedicated to handwritten char- acter recognition. The scheme we propose relies on a hidden layer which extracts feature vectors on a fixed number of points of interest, and an output ...

2009

Retrieving Ancient Maya Glyphs with Shape Context

Daniel Gatica-Perez, Jean-Marc Odobez, Edgar Roman Rangel

We introduce an interdisciplinary project for archaeological and computer vision research teams on the analysis of the ancient Maya writing system. Our first task is the automatic retrieval of Maya syllabic glyphs using the Shape Context descriptor. We inv ...

IEEE2009

CLEF2008 Image Annotation Task: an SVM Confidence-Based Approach

Barbara Caputo, Tatiana Tommasi

This paper presents the algorithms and results of our participation to the medi- cal image annotation task of ImageCLEFmed 2008. Our previous experience in the same task in 2007 suggests that combining multiple cues with diﬀerent SVM-based approaches is ve ...

Idiap2008

Cue Integration for Medical Image Annotation

Barbara Caputo, Tatiana Tommasi

This paper presents the algorithms and results of our par- ticipation to the image annotation task of ImageCLEFmed 2007. We proposed a multi-cue approach where images are represented both by global and local descriptors. These cues are combined following t ...

Springer-Verlag2008

Multi-layer Boosting for Pattern Recognition

François Fleuret

We extend the standard boosting procedure to train a two-layer classifier dedicated to handwritten character recognition. The scheme we propose relies on a hidden layer which extracts feature vectors on a fixed number of points of interest, and an output l ...

Idiap2008