 A texture thesaurus for browsing large aerial photographsWei‐Ying Ma and B. S. Manjunath Journal of the American Society for Information Science, 1998, vol. 49, issue 7, 633-648 Abstract: A texture‐based image retrieval system for browsing large‐scale aerial photographs is presented. The salient components of this system include texture feature extraction, image segmentation and grouping, learning similarity measure, and a texture thesaurus model for fast search and indexing. The texture features are computed by filtering the image with a bank of Gabor filters. This is followed by a texture gradient computation to segment each large airphoto into homogeneous regions. A hybrid neural network algorithm is used to learn the visual similarity by clustering patterns in the feature space. With learning similarity, the retrieval performance improves significantly. Finally, a texture image thesaurus is created by combining the learning similarity algorithm with a hierarchical vector quantization scheme. This thesaurus facilitates the indexing process while maintaining a good retrieval performance. Experimental results demonstrate the robustness of the overall system in searching over a large collection of airphotos and in selecting a diverse collection of geographic features such as housing developments, parking lots, highways, and airports. © 1998 John Wiley & Sons, Inc. Date: 1998 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jamest:v:49:y:1998:i:7:p:633-648 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Journal of the American Society for Information Science from Association for Information Science & Technology |
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