Image organization and retrieval with automatically constructed feature vectors

effectively store and retrieve them based on their contents. Retrieving images based on their contents is a difficult problem. Instead of using manually assigned content descriptors for retrieval, we take an approach where images are indexed and organized automatically so that the users can retrieve images by visually browsing the organized image space. For image indexing, the objects in an image are first analyzed for their shape features such as roundness, rectangularity, elipticit y, eccentricity, bending energy. These features are used to form a feature vector that represents the rmage. Subsequently, the feature vectors representing all the images in a image database are organized by a unsupervised neural net learning algorithm called Self-Organizing Map. Since this image feature map reflects the statistical patterns, i.e., the inter-similarities of the objects, the relationships among the images can be recognized by their location, neighborhood, and the way the map is organized. For the feasibdity and practicality of the approach, a prototype system has been developed and tested with some experiments. 1