NeTra: A toolbox for navigating large image databases. We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object- or region-based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Images are segmented into homogeneous regions at the time of ingest into the database, and image attributes that represent each of these regions are computed. In addition to image segmentation, other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as “retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper of the image”, where the individual objects could be regions belonging to different images. A Java-based web implementation of NeTra is available at

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  1. Shao, Zhenfeng; Li, Deren; Zhu, Xianqiang: A multi-scale and multi-orientation image retrieval method based on rotation-invariant texture features (2011)
  2. Bartolini, Ilaria; Ciaccia, Paolo; Patella, Marco: Query processing issues in region-based image databases (2010)
  3. Di Mascio, Tania; Frigioni, Daniele; Tarantino, Laura: VISTO: a new CBIR system for vector images (2010)
  4. Gondra, Iker; Xu, Tao: Image region re-weighting via multiple instance learning (2010)
  5. Huang, Wei; Gao, Yan; Chan, Kap Luk: A review of region-based image retrieval (2010)
  6. Zhang, Shile; Li, Bin; Xue, Xiangyang: Semi-automatic dynamic auxiliary-tag-aided image annotation (2010)
  7. Belkhatir, Mohammed: An operational model based on knowledge representation for querying the image content with concepts and relations (2009)
  8. Ren, W.; Singh, S.; Singh, M.; Zhu, Y.S.: State-of-the-art on spatio-temporal information-based video retrieval (2009)
  9. Ji, Rongrong; Yao, Hongxun; Liang, Dawei: DRM: Dynamic region matching for image retrieval using probabilistic fuzzy matching and boosting feature selection (2008)
  10. Liu, Ying; Zhang, Dengsheng; Lu, Guojun: Region-based image retrieval with high-level semantics using decision tree learning (2008)
  11. Andreou, Ioannis; Sgouros, Nikitas M.: Utilizing shape retrieval in sketch synthesis (2007)
  12. Choraś, Ryszard S.; Andrysiak, Tomasz; Choraś, Michał: Integrated color, texture and shape information for content-based image retrieval (2007)
  13. Liu, Ying; Zhang, Dengsheng; Lu, Guojun; Ma, Wei-Ying: A survey of content-based image retrieval with high-level semantics (2007)
  14. Marques, Oge; Mayron, Liam M.; Borba, Gustavo B.; Gamba, Humberto R.: An attention-driven model for grouping similar images with image retrieval applications (2007)
  15. Park, Sang-Sung; Seo, Kwang-Kyo; Jang, Dong-Sik: Fuzzy art-based image clustering method for content-based image retrieval (2007)
  16. Rege, Manjeet; Dong, Ming; Fotouhi, Farshad: Building a user-centered semantic hierarchy in image databases (2007)
  17. Stentiford, Fred: Attention-based similarity (2007)
  18. Yoo, Hun-Woo; Cho, Sung-Bae: Video scene retrieval with interactive genetic algorithm (2007)
  19. Zajić, Goran; Kojić, Nenad; Radosavljević, Vladan; Rudinac, Maja; Rudinac, Stevan; Reljin, Nikola; Reljin, Irini; Reljin, Branimir: Accelerating of image retrieval in CBIR system with relevance feedback (2007)
  20. Fauqueur, Julien; Boujemaa, Nozha: Mental image search by boolean composition of region categories (2006)

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