We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image; secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application/task-driven saliency, if appropriately annotated images are available.

We propose a methodology to analyze and visualize the relationships and influences between painters. We build a graph where each painter is a node and an edge between two nodes is weighted by the painters' similarity. The similarity of two painters is measured as a function of the similarity between their paintings. Although the image representation we use was initially developed for the detection of objects in natural images, we will show how our system can discover artistic periods and non-obvious connections between painters belonging to different artistic movements, showing its usefulness for the computer-assisted study of art.

A face recognition algorithm based on a iterated k-means classification technique will be presented in this paper. The proposed algorithm, when compared with popular PCA algorithms for face recognition has an improved recognition rate on various benchmark datasets. The presented algorithm, unlike PCA, is not a dimensional reduction algorithm, nonetheless it yields barycentric-faces which can be used to determine different types of face expressions, light conditions and pose. The accuracy of PCA and k-means methods has been evaluated under varying expression, illumination and pose using standard face databases.

Classification is one of the most widely used method in data mining with numerous applications in biomedicine. The scope and the resolution of data involved in many real life applications require very efficient implementations of classification methods, developed to run on parallel or distributed computational systems. In this study, a parallel implementation of an efficient algorithm that is based on regularized general eigenvalue classification is introduced. The proposed implementation is tested on a very large scale genomic data base and preliminary results regarding efficiency are presented.