wednesday 24 november 2010
I will be in Brazil for the next two weeks. I've been invited for a talk at UFMG scheduled on Friday 03/12/21010 morning at UFMG, Belo Horizonte:
Content based multimedia retrieval and indexing
In multimedia content based retrieval, we usually tackle three different user aims when using the system: the search for a target document, the interactive browsing of a multimedia database, and the categorization of an arbitrary number of documents into predefined classes. This talk will provide an overview of my work in these three areas. The first part will be on the retrieval of urban scene photographs (as part of the french iTowns project). The second part will be on active statistical learning tools for interactive browsing, especially in the context of browsing distributed databases and collaborative systems. The last part will be on image categorization, where I will present machine learning techniques for efficiently combining multiple description channels such as shape, color and texture.
monday 15 november 2010
MotivationMachine learning techniques are now widely used in Content-Based Multimedia Indexing (CBMI) systems to bridge the semantic gap. Most of these methods are designed around a single user, who is invited to build a query made of image annotations. Depending on the difficulty of the searched concept, the user gives more and more annotations, until he is satisfied by the results. In such a scenario, better retrieval systems are those which return better results with less annotations.
Then, once such systems are deployed, many users perform their searches independently. However, it is common that several users are looking for similar visual concepts. That means that a lot of knowledge can be shared among users. In other words, if previous users already found several concepts, a smart retrieval system should take advantage of this previous knowledge in order to speed up the next retrieval sessions.
This problematic is poorly addressed by the content based multimedia retrieval community. The aim of this special session is to first present the latest methods in this scope, but also to invite new researchers to address these learning problems.
Scope and topicsThe scope of this session is to cover innovative indexing systems which break the single user paradigm. Such systems are facing many learning problems. In that case, the main question is how to efficiently combine these learning tasks to improve the results. A first example would be the collaborative learning of a single concept and a second one the long-term learning of unknown concepts.
The special session seeks for papers describing original work in the following areas:
- multi-users retrieval systems
- collaborative learning of a single concept
- long-term learning of unknown concepts
- fusion of retrieval sessions
- concepts discovery
- active learning in collaborative learning context
- Philippe Henri Gosselin, ETIS Lab - ENSEA, France
- David Picard, ETIS Lab - ENSEA, France