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We describe building a large-scale image ontology using the WordNet lexical resources. This ontology is based on English words identifying portrayable objects. We reviewed the upper structure and interconnections of WordNet and selected only the branches leading to portrayable objects. This article explains our pruning approach to WordNet. The words, which(More)
When building an e-learning system, we need a way of organizing the information to be presented to the user. Formal ontologies are one way of resolving this knowledge structuring task. The main use of ontologies in e-learning systems concerns the modelling of the domain of interest and this is equally our approach. Here we present a technique for(More)
As well as words in text processing, image regions are poly-semic and need some disambiguation. If the set of representations of two different objects are close or intersecting, a region that is in the intersection will be recognized as being possibly both objects. We propose here a way to disambiguate regions using some knowledge on relative spatial(More)
In our effort to contribute to the closing of the "semantic gap" between images and their semantic description, we are building a large-scale ontology of images of objects. This visual catalog will contain a large number of images of objects, structured in a hierarchical catalog, allowing image processing researchers to derive signatures for wide classes of(More)
Shared evaluation tasks have become popular over the last decades as ways of making communities of researchers advance together. This paper presents the organization of five new shared task evaluation campaigns for image indexing and retrieval. We have designed these campaigns based on our previous experience of participating in or organizing various text(More)
In this paper, we propose to improve our previous work on automatically filling an image ontology via clustering using images from the web. This work showed how we can automatically create and populate an image ontology using the WordNet textual ontology as a basis, pruning it to keep only portrayable objects, and clustering to get representative image(More)
An annotation of 59 795 images from the Corel database is presented. Each image has been labelled as containing an animal or not. The type of animal is also specified for most images. In addition, 1289 images have been manually segmented into animals and background. This annotated dataset will allow the evaluation of segmentation, feature extraction and(More)