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Comparing global and interest point descriptors for similarity retrieval in remote sensed imagery

Published: 07 November 2007 Publication History

Abstract

We investigate the application of a new category of low-level image descriptors termed interest points to remote sensed image analysis. In particular, we compare how scale and rotation invariant descriptors extracted from salient image locations perform compared to proven global texture features for similarity retrieval. Qualitative results using a geographic image retrieval application and quantitative results using an extensive ground truth dataset show that interest point descriptors support effective similarity retrieval in large collections of remote sensed imagery.

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      cover image ACM Other conferences
      GIS '07: Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
      November 2007
      439 pages
      ISBN:9781595939142
      DOI:10.1145/1341012
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 07 November 2007

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      Author Tags

      1. image retrieval
      2. interest points
      3. remote sensed imagery
      4. similarity search

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