Project-Team Imedia Images and Multimedia: Indexing, Retrieval and Navigation

More generally, the research work and the academic and industrial collaborations of the IMEDIA team aim to answer the complex problem of the intelligent access to multimedia content. queries (tradeoff between the access time and the cost of storage). In this step, to address problems that have been dealt with for some time in the database community, in a new context: image databases. of the to speciﬁc methods for structuring each of these Our research on object detection addresses the design of invariant kernels and algorithmically efﬁcient solutions. We have developed several algorithms for face detection based on a hierarchical combination of simple two-class classiﬁers. Such architectures concentrate the computation on ambiguous parts of the scene and achieve error rates as good as those of far more expensive techniques. The computational efﬁciency we are looking for has the effect of a regularization constraint: it favors structurally simple classiﬁers, which have good generalization properties. invariance of matching schemes to the view-based representation underlying support machines or other the competitive agglomeration that allows us to cope with our primary requirements: estimate the unknown number of classes, handle noisy data and deal with classes (by using fuzzy memberships that delay the decision as much as possible). (cid:15) allow the user to notify his satisfaction (or not) on the system retrieval results–method commonly called relevance feedback. In this case, the user reaction expresses more generally a subjective preference and therefore can compensate for the semantic gap between visual appearance and the user intention, provide precise visual query formulation that allows the user to select precisely its region of interest and pull off the image parts that are not representative of his visual target, (cid:15) provide a mechanism to search for the user mental image when no starting image example is available. Several approaches are investigated. As an example, we can mention the logical composition from visual thesaurus. Besides, learning methods related to information theory are also developed for efﬁcient relevance feedback model in several context study including mental image retrieval. Telecommunications Examples: image representation and content-based queries stand as the basis of MPEG-4 and MPEG-7. IMEDIA does not contribute to their normative aspects but is interested in the latest results related to the MPEG-7 group. Note that the signatures developped by IMEDIA can be used with this norm. only to a range of rotation angles. This is illustrated in Fig. 9(b) where the best results are obtained with the jet descriptors, rather than using Hilbert invariants which are invariants to rotation too. This comparison was performed using the Columbia color database, where objects differ only from small image rotation. We use SVM-based RF with both the RBF kernel and the angular (or not compact triangular) kernel. Among the labeling strategies, we consider various partial labeling methods, as well as making mistakes while labeling. We ﬁrst ﬁnd that the ranking between MP and MAO does not depend much on the selected strategy. Second, the angular kernel performs constantly better than the RBF one, and the difference increases with the complexity of the classes. Third, the ranking between strategies appears to be relatively independent of the complexity (roughly corresponding to the semantic level) of the ground-truth classes. This robustness property with respect to variations in the user strategy and in the complexity of the database is a very desirable property when designing relevance feedback systems that should be effective for most of the users. We suggest that comparisons between RF algorithms should be conducted with several different strategies to evaluate the stability of the results with respect to changes in the user strategy. the attractiveness of our selection strategy. Moreover, when the target of RF is the batch classiﬁcation of images, the MAO selection criterium also performs better in terms of classiﬁcation error and false positives rate compared to MP and MPO criteria (see [10]). The same conclusions remains true when using RF with databases of image regions (see [9]).