Hierarchical image database browsing environment with embedded relevance feedback

We address the user-navigation through large volumes of image data. A tree structured K-means clustering is introduced which will hierarchically group images into similar groups. Providing the nodes of the different levels with representative image samples leads to different "image maps" similar to street maps with various resolutions of details. The user can zoom into various cluster levels to obtain more or less detail 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 relevance feedback and the hierarchical structure together with a three-dimensional visualization of the "image maps" leads to an intuitive browsing environment. The results obtained verify the attractiveness of the approach for navigation and retrieval applications.