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Multimedia search engines are often based on multiple decentralized search services, multiple information sources (text search, audio search, visual search, semantic search engines, etc.), multiple data representation and similarity measures. Heterogeneous multiple search results need to be combined and structured efficiently and generically. In this paper, we propose a new multiple search results...
Heterogeneous multiple search results need to be combined and structured efficiently and generically. In this paper, we propose a new multiple search results clustering algorithm based on the relevant set correlation model. As the model is based on shared neighborhood information only, it allows our new technique to process the different information sources as simple oracles returning ranked lists...
The bag-of-visual-words is a popular representation for images that has proven to be quite effective for automatic annotation. In this paper, we extend this representation in order to include weak geometrical information by using visual word pairs. We show on a standard benchmark dataset that this new image representation improves significantly the performances of an automatic annotation system.
We propose a non-homogeneous conditional random field built over an adjacency graph of superpixels for contextual classification of high-resolution satellite images. By introducing the contextual histogram descriptor, our model includes spatially dependent unary and pairwise potentials that capture contextual interactions of the data as well as the labels. This results the non-homogeneity of the fields...
We propose a non-homogeneous conditional random field (CRF) built over an adjacency graph of superpixels for contextual region grouping. Our model includes spatially dependent potentials that capture contextual interactions of the data as well as the labels. Both superpixels and segments are described with local statistics which take into account their contexts in the image. This results the non-homogeneity...
In this paper, we present a probabilistic framework for edge and region grouping using conditional random field. Our model is built on a hybrid adjacency graph of atomic region and contour primitives. Unary and pairwise potentials that capture similarity, proximity and curvilinear continuity are defined. Similarity, for both region and edge cues, is measured by likelihood ratios learned from a human...
Tversky's set-theoretic similarity states that a similarity measure should increase with the saliency of common features and decrease with that of distinctive features. When all necessary and relevant semantic features could be listed by hand, the similarity measure would be reduced to count the number of common features followed by subtracting the number of distinctive features. The reason is one...
Kernel based methods such as support vector machine (SVM) has provided successful tools for solving many recognition problems. One of the reasons of this success is the use of kernels. Positive definiteness has to be checked for kernels to be suitable for most of these methods. For instance for SVM, the use of a positive definite kernel insures that the optimized problem is convex and thus the obtained...
We introduce in this paper a new formulation of the regularized fuzzy c-means (FCM) algorithm which allows us to find automatically the actual number of clusters. The approach is based on the minimization of an objective function which mixes, via a particular parameter, a classical FCM term and a new entropy regularizer. The main contribution of the method is the introduction of a new exponential...
Traditional clustering algorithms usually rely on a pre-defined similarity measure between unlabelled data to attempt to identify natural classes of items. When compared to what a human expert would provide on the same data, the results obtained may be disappointing if the similarity measure employed by the system is too different from the one a human would use. To obtain clusters fitting user expectations...
This contribution proposes a fuzzy approach to color image filtering by the fuzzy modeling of the concept of color credibility. Based on the perceptual notion of color resemblance, the colors are modeled as fuzzy sets in the CIELAB color space. The filtering principle is to select at the filters output the color that is the most credible with respect to the rest of the colors within the filtering...
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