Visual Data Mining

This paper introduces a novel paradigm for integrated retrieval and browsing in content-based visual information retrieval systems. The proposed approach uses feature transformations and distance measures for content-based media access and similarity measurement. The first innovation is that distance space is visualised in a 3D user interface: 2D representations of media objects are shown on the image plane. The floor plane is used to show their distance relationships. Queries can interactively be defined by browsing through the 3D space and selecting media objects as positive or negative examples. Each selection operation defines hyper-clusters that are used for querying, and causes query execution and distance space adaptation in a background process. In order to help the user understanding distance space, descriptions are visualised in diagrams and associated with media objects. Changes in distance space are visualised by tree-like graphs. Furthermore, the user is enabled to select subspaces of distance space and select new distance metrics for them. This allows dealing with multiple similarity judgements in one retrieval process. The proposed components for visual data mining will be implemented in the visual information retrieval project VizIR. All VizIR components can be arbitrarily combined to sophisticated retrieval applications.