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A Discriminative Kernel-based Model to Rank Images from Text Queries

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Kernel Methods for Melanoma Recognition

Barbara Caputo, Tatiana Tommasi

Malignant melanoma is the most deadly for of skin lesion. Early diagnosis is of critical importance to patient survival. Visual recognition algorithms could potentially be of great help for physicians in a computer assisted diagnosis system. Previous work ...

2006

Information Theoretic Combination of Classifiers With Application to Multiple SVMs

Jean-Philippe Thiran, Julien Meynet

Combining several classifiers has proved to be an efficient machine learning technique. Two concepts influence clearly the efficiency of an ensemble: the diversity between classifiers and the individual accuracies of the classifiers. We use an information ...

2006

A Discriminative Approach for the Retrieval of Images from Text Queries

Samy Bengio, David Grangier, Florent Monay Michaud

This work proposes a new approach to the retrieval of images from text queries. Contrasting with previous work, this method relies on a discriminative model: the parameters are selected in order to minimize a loss related to the ranking performance of the ...

2006

A Kernel Classifier for Distributions

Samy Bengio, Alexei Pozdnoukhov

This paper presents a new algorithm for classifying distributions. The algorithm combines the principle of margin maximization and a kernel trick, applied to distributions. Thus, it combines the discriminative power of support vector machines and the well- ...

IDIAP2005

Using Highly Discriminative Keys for Indexing in a Peer-to-Peer Full-Text Retrieval System

Karl Aberer, Martin Rajman, Vinh Toan Luu, Fabius Klemm

Excessive network bandwidth consumption, caused by the transmission of long posting lists, was identified as one of the major bottlenecks for implementing distributed full-text retrieval in a Peer-to-Peer (P2P) architecture. To address this problem we intr ...

2005

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...

2004

Text detection and recognition in images and video sequences

Datong Chen

Text characters embedded in images and video sequences represents a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, gra ...

2003