Content-Based (Unsupervised) Image Segmentation for Large-Scale Spatial Images (with MATLAB )

• Published in: 2006 Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory pp. 215 - 219
• Main Authors: Luo, J., Cross, J.E.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2006
• Subjects: Image analysis; Image generation; Image retrieval; Image segmentation; Information analysis; Information retrieval; Large-scale systems; MATLAB; National security; Organizing
• ISBN: 9780780394575; 0780394577
• ISSN: 0094-2898; 2161-8135
• DOI: 10.1109/SSST.2006.1619075

Enormous volumes of image data were generated everyday, but can't be used unless they are organized so as to allow efficient browsing, searching and retrieval. On the other hand, the image data are not only required for developing spatial or geographic data, but they are also critical for military and intelligence applications especially suited to addressing national security, that is, image data are critical for situation awareness and assessment purposes, and they are invaluable for detecting changes and providing relevant information to decision makers. Currently, there is a lack of comprehensive tools that can allow fast and efficient processing of information from huge image data. In order to do automated image retrieval to meet the challenge of organizing and analyzing vast volumes of image data to effectively synthesize the critical information, the key step is image segmentation for the large-scale spatial image. In this paper, one content-based (unsupervised) image segmentation method is proposed and tested out to be very successful for the large-scale image segmentation