When: April 11th, 2013 from 11AM to noon.
Where: room B21
Speaker: Cristina Manfredotti, LIP6 UPMC (Paris 6)
Title: Relational Inference and Learning for Complex Recognition Tasks
Abstract:
Many domains in the real world are richly structured, containing a multitude of entities related to each other in a variety of ways. Many problem domains require modeling the behaviors of multiple agents, understanding their roles, the context and detecting anomalies. Examples of such domains span from surveillance systems (in which, for example, one has to identify the activity of multiple interacting agents) to systems for marketing support (discount campaigns targeting specific key customers, product recommendations based on similar purchases made by others, etc.) and from bio-informatics (relationships between patients’ genetic profiles and their drug responses) to human motion understanding (relationships between active components, such as joints in body motion analysis). A key characteristic of many situations is that interactions (or relations) are dynamic and may change over time. The explicit recognition of the relationships between entities can improve the understanding of their behaviors, and help predicting future trends.
In this talk we will cover the problems of (1) modeling dynamically changing relations between entities, (2) making inference in dynamic domains taking into account relations between entities and (3) learning probabilistic models of interaction of entities. In order to automatically learn probabilistic models that take into account dinamically changing relations we propose a semi-supervised learning framework that makes use of hierarchical abstraction. Experimental results on the particular application of multi target tracking and (online) activity recognition will be considered. These show that our approach decreases the tracking error rate, improves the data association performance and identifies the correct activity with higher accuracy than standard approaches.
