Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper describes a content-based image retrieval digital library that supports geographical image retrieval over a testbed of 800 aerial photographs, each 25 megabytes in size. In addition, this paper also introduces a methodology to evaluate the performance of the algorithms
in the prototype system. The major contributions of this paper are two. 1) We suggest an approach that incorporates various image processing techniques including Gabor filters, image enhancement, and image compression, as well as information analysis technique such as self-organizing map (SOM) into an effective large-scale geographical image retrieval system. 2) We present two experiments that evaluate the performance of the Gaborfilter- extracted features along with the corresponding similarity measure
against that of human perception, addressing the lack of studies in assessing the consistency between an image representation algorithm or an image categorization method and human mental model.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105813 |
| Date | 01 1900 |
| Creators | Zhu, Bin, Ramsey, Marshall C., Chen, Hsinchun |
| Publisher | IEEE |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
Page generated in 0.002 seconds