Content-Based Image Retrieval in Astronomy

Content-based image retrieval in astronomy needs methods that can deal with an image content made of noisy and diffuse structures. This motivates investigations on how information should be summarized and indexed for this specific kind of images. The method we present first summarizes the image information content by partitioning the image in regions with same texture. We call this process texture summarization. Second, indexing features are generated by examining the distribution of parameters describing image regions. Indexing features can be associated with global or local image characteristics. Both kinds of indexing features are evaluated on the retrieval system of the Zurich archive of solar radio spectrograms. The evaluation shows that generating local indexing features using self-organizing maps yields the best effectiveness of all tested methods.

• Type: Journal
• Year: 2000
• Where: IR
• Authors: André Csillaghy, Hans Hinterberger, A. O. Benz
• Keywords: Content-based Image Retrieval, Image, Indexing Features, IR 2000, Natural Language Processing