Exploration of artworks is enjoyable but often time consuming. For example, it is not always easy to discover the favorite types of unknown painting works. It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists. This paper presents a painting image browser which assists the explorative discovery of user-interested painting works. The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images. This study assumes a large number of painting images are provided where categorical information (e.g., names of artists, created year) is assigned to the images. The presented system firstly calculates the feature values of the images as a preprocessing step. Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information. This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works. Our case study and user evaluation demonstrates the effectiveness of the presented image browser.
Research of protein is pivotal to the drug discovery, since most of the drugs act upon proteins inside human body.Drugs act when they are close to the concave portions, so called "pockets", of the protein surfaces. Therefore, detection and analysis of the pockets are also important for the drug discovery. This paper presents a fast method with pocket extraction and evaluation technique for the protein surfaces. When the protein surfaces are provided as triangular meshes, the method first applies mesh simplification to smoothing small geometric features. It then detects concave portions as pockets from the simplified triangular meshes. The method then evaluates the pockets from the following viewpoints: geometry and chemistry. This paper introduces our case study which applied the presented technique to 60 proteins, and successfully visualized appropriate druggability estimation and correlation between chemical properties and druggability.
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