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Cataloged from PDF version of article. The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classi- fication. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback–Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes.

Date of Conference: 25-27 June 2007 Conference name: International Workshop on Content-Based Multimedia Indexing, 2007 The management and exchange of multimedia data is challenging due to the variety of formats, standards and intended applications. In addition, production of multimedia data is rapidly increasing due to the availability of off-the-shelf, modern digital devices that can be used by even inexperienced users. It is likely that this volume of information will only increase in the future. A key goal of the MUSCLE (Multimedia Understanding through Semantics, Computation and Learning) network is to develop tools, technologies and standards to facilitate the interoperability of multimedia content and support the exchange of such data. One approach for achieving this was the creation of a specific "E-Team", composed of the authors, to discuss core questions and practical issues based on the participant's individual work. In this paper, we present the relevant points of view with regards to sharing experiences and to extracting and integrating multimedia data and metadata from different modes (text, images, video). © 2007 IEEE.

Adapting to user context, individual features and behaviour patterns is a topic of great attention nowadays in the field of Web-based and mobile learning. A challenge is to design personalized interfaces and software enabling easy access to the learning content while being sufficiently flexible to handle changes in a user's context, perception and available resources. This paper presents a Web-based adaptation and personalization system, AdaptiveWeb, that uses cognitive aspects as its core filtering element. The proposed system focuses upon the creation of a comprehensive user profiling that combines parameters that analyze the most intrinsic users' characteristics like visual, cognitive, and emotional processing parameters as well as the "traditional" user profiling characteristics and together tend to give an optimized adapted and personalized result to the user. The use of semantics enables the openness of the system as adequately described in the paper. © 2007 IEEE. 68 73 Conference code: 72636 Cited By :7