We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training e...
We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding tasks (multi-class classification). Existing multi-class a...
This paper presents a new algorithm based on boosting for interactive object retrieval in images. Recent works propose ”online boosting” algorithms where weak classifier sets...
Alexis Lechervy, Philippe Henri Gosselin, Frederic...
In this work, we explore the use of a learning-based framework for retrieval of relevant mammogram images from a database, for purposes of aiding diagnoses. A fundamental issue is...
Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Niko...
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...