PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semantic lattices for multiple annotation of images
Anne-Marie Tousch, Stéphane Herbin and Jean-Yves Audibert
In: MIR 2008, 30-31 Oct 2008, Vancouver, Canada.


We address the problem of describing precisely an object present in an image. The starting point is a semantic lattice defining all possible coherent object descriptions through inheritance and exclusion relations. This domain knowledge is used in a learning process which outputs a set of coherent explanations of the image valued by their confidence level. Our first contribution is to design this method for multiple complexity level image description. Our secondary focus is to develop rigorous evaluation standards for this computer vision task which, to our knowledge, has not been addressed in the literature despite its possible use in symbolic annotation of multimedia database. A critical evaluation of our approach under the proposed standards is presented on a new appropriate car database that we have collected.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:5073
Deposited By:Jean-Yves Audibert
Deposited On:24 March 2009