Chabot: retrieval from a relational database of images. Selecting from a large, expanding collection of images requires carefully chosen search criteria. We present an approach that integrates a relational database retrieval system with a color analysis technique. The Chabot project was initiated at our university to study storage and retrieval of a vast collection of digitized images. These images are from the State of California Department of Water Resources. The goal was to integrate a relational database retrieval system with content analysis techniques that would give our querying system a better method for handling images. Our simple color analysis method, if used in conjunction with other search criteria, improves our ability to retrieve images efficiently. The best result is obtained when text-based search criteria are combined with content-based criteria and when a coarse granularity is used for content analysis.

References in zbMATH (referenced in 38 articles )

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  1. Huang, Wei; Gao, Yan; Chan, Kap Luk: A review of region-based image retrieval (2010) ioport
  2. Lin, Chia-Chen; Wang, Shing-Shoung: An edge-based image copy detection scheme (2008)
  3. Anstee, Richard; Sali, Attila: Latin squares and low discrepancy allocation of two-dimensional data (2007)
  4. Liu, Ying; Zhang, Dengsheng; Lu, Guojun; Ma, Wei-Ying: A survey of content-based image retrieval with high-level semantics (2007)
  5. Chow, Tommy W.S.; Rahman, M.K.M.; Wu, Sitao: Content-based image retrieval by using tree-structured features and multi-layer self-organizing map (2006) ioport
  6. Kim, Chang-Ryong; Chung, Chin-Wan: XMage: an image retrieval method based on partial similarity (2006)
  7. Ksantini, R.; Ziou, D.; Dubeau, F.: Image retrieval based on region separation and multiresolution analysis (2006)
  8. Mittal, Ankush: An overview of multimedia content-based retrieval strategies (2006)
  9. Torres, Ricardo da S.; Medeiros, Claudia Bauzer; Gonçcalves, Marcos André; Fox, Edward A.: A digital library framework for biodiversity information systems (2006) ioport
  10. Adachi, Fuminori; Washio, Takashi; Fujimoto, Atsushi; Motoda, Hiroshi; Hanafusa, Hidemitsu: Multi-structure information retrieval method based on transformation invariance (2005) ioport
  11. Duan, Lijuan; Gao, Wen; Zeng, Wei; Zhao, Debin: Adaptive relevance feedback based on Bayesian inference for image retrieval (2005)
  12. Kim, N.W.; Kim, T.Y.; Choi, Jong Soo: Edge-based spatial descriptor using color vector angle for effective image retrieval (2005)
  13. Qi, Xiaojun; Han, Yutao: A novel fusion approach to content-based image retrieval (2005) ioport
  14. Zhang, Ruofei; Zhang, Zhongfei Mark: FAST: Toward more effective and efficient image retrieval (2005) ioport
  15. Chen, Yixin; Li, Jia; Wang, James Z.: Machine learning and statistical modeling approaches to image retrieval. (2004)
  16. Ahmad, Khurshid; Tariq, Mariam; Vrusias, Bogdan; Handy, Chris: Corpus-based thesaurus construction for image retrieval in specialist domains (2003)
  17. Pun, Chi-Man: Rotation-invariant texture feature for image retrieval. (2003)
  18. Sebe, Nicu; Lew, Michael S.: Robust computer vision. Theory and applications. (2003)
  19. Xu, Y.; Duygulu, P.; Saber, E.; Tekalp, A.M.; Yarman-Vural, F.T.: Object-based image labeling through learning by example and multi-level segmentation (2003)
  20. Antani, Sameer; Kasturi, Rangachar; Jain, Ramesh: A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video (2002)

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