In recent years, there has been a growing interest in developing effective methods for searching large image databases based on image content. A commonly used method is search-by-query, that is often not satisfactory. Often it is difficult to find or produce good query images or repetitive queries tend to become trapped among a small group of undesirable images. To overcome these problems the user is to be provided with easy and intuitive access to information in image databases. In this paper we present a new browsing environment, which uses the metaphor of maps. Like street maps with different scales, from a world map to a city map, the image space is represented through
Multimedia database interfaces should be designed to be very user-adaptive, since there is no generally applicable model of user's search behavior or of his search intention. First, the challenging task for the interface is to present the most representative objects in an appealing and concise manner. Second, the interface has to identify the user's search intention from very few positive feedbacks. In particular for the latter there exist a lot of Relevance Feedback imple-mentations.
While most of them are considered as more or less heuristically proved parameter adjustment procedures, we treat Relevance Feedback as direct probability density estimation. Our density is defined as the
n 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.
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