3D browsing environment for MPEG-7 image databases

In this paper we address the user-navigation through large volumes of image data. Similarity-measures based on different MPEG-7 descriptors are introduced and multidimensional scaling is employed to display images in three dimensions according to their mutual similarities. With such a view the user can easily see similarity relations between images and understand the structure of the database. In order to cope with large volumes of images a k-means clustering technique is introduced which identifies representative image samples for each cluster. Representative images (up to 100) are then displayed in three dimensions using multidimensional scaling structuring. The clustering technique proposed produces a hierarchical structure of clusters-similar to street maps with various resolutions of details. The user can zoom into various cluster levels to obtain more or less details if required. Further a new query refinement method is introduced. The retrieval process is controlled by learning from positive examples from the user, often called the relevance feedback of the user. The combination of the three techniques 3D-visualization, relevance feedback and the hierarchical structure of the image database leads to an intuitive browsing environment. The results obtained verify the attractiveness of the approach for navigation and retrieval applications.