As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being...
We present a novel pedestrian detection system based on probabilistic component assembly. A part-based model is proposed which uses three parts consisting of head-shoulder, torso a...
Martin Rapus, Stefan Munder, Gregory Baratoff, Joa...
This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus a...
In this paper we explicitly identify the probabilistic model underlying LCS by linking it to a generalisation of the common Mixture-of-Experts model. Having an explicit representa...
In this paper we present some results obtained in humour classification over a corpus of Italian quotations manually extracted and tagged from the Wikiquote project. The experimen...