The QBIC project: querying image by content using color, texture and shape. In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images’ content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one’), photo-journalism (`Give me images that have blue at the top and red at the bottom’), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.

References in zbMATH (referenced in 36 articles )

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  1. Baier, Daniel; Frost, Sarah: Relating brand confusion to ad similarities and brand strengths through image data analysis and classification (2018)
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  4. Min, Rui; Cheng, H. D.: Effective image retrieval using dominant color descriptor and fuzzy support vector machine (2009)
  5. Chiang, Cheng-Chin; Wu, Jyun-Yue; Yang, Mau-Tsuen; Tai, Wen-Kai: Independent query refinement and feature re-weighting using positive and negative examples for content-based image retrieval (2008) ioport
  6. Ji, Rongrong; Yao, Hongxun; Liang, Dawei: DRM: Dynamic region matching for image retrieval using probabilistic fuzzy matching and boosting feature selection (2008)
  7. Thomasian, Alexander; Li, Yue; Zhang, Lijuan: Optimal subspace dimensionality for k-nearest-neighbor queries on clustered and dimensionality reduced datasets with SVD (2008) ioport
  8. Rahman, M. K. M.; Yang, Wang Pi; Chow, Tommy W. S.; Wu, Sitao: A flexible multi-layer self-organizing map for generic processing of tree-structured data (2007)
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  11. 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
  12. Huang, Szu-Hao; Wu, Qi-Jiunn; Lai, Shang-Hong: Improved adaBoost-based image retrieval with relevance feedback via paired feature learning (2006) ioport
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  15. Zhang, Dengsheng; Lu, Guojun: Review of shape representation and description techniques (2004) ioport
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  17. Wang, Ying-Hong: Image indexing and similarity retrieval based on spatial relationship model. (2003) ioport
  18. Cho, Sung-Bae: Towards creative evolutionary systems with interactive genetic algorithm (2002)
  19. Mokhtarian, Farzin; Abbasi, Sadegh: Shape similarity retrieval under affine transforms (2002)
  20. Vleugels, Jules; Veltkamp, Remco C.: Efficient image retrieval through vantage objects (2002)

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