PicToSeek: combining color and shape invariant features for image retrieval. We aim at combining color and shape invariants for indexing and retrieving images. To this end, color models are proposed independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived from which shape invariant features are computed. Computational methods are described to combine the color and shape invariants into a unified high-dimensional invariant feature set for discriminatory object retrieval. Experiments have been conducted on a database consisting of 500 images taken from multicolored man-made objects in real world scenes. From the theoretical and experimental results it is concluded that object retrieval based on composite color and shape invariant features provides excellent retrieval accuracy. Object retrieval based on color invariants provides very high retrieval accuracy whereas object retrieval based entirely on shape invariants yields poor discriminative power. Furthermore, the image retrieval scheme is highly robust to partial occlusion, object clutter and a change in the object’s pose. Finally, the image retrieval scheme is integrated into the PicToSeek system on-line at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching images on the World Wide Web.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 36 articles )

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  1. Di Mascio, Tania; Frigioni, Daniele; Tarantino, Laura: VISTO: a new CBIR system for vector images (2010)
  2. Gijsenij, Arjan; Gevers, Theo; van de Weijer, Joost: Generalized gamut mapping using image derivative structures for color constancy (2010)
  3. Huang, Wei; Gao, Yan; Chan, Kap Luk: A review of region-based image retrieval (2010)
  4. Liu, Guang-Hai; Zhang, Lei; Hou, Ying-Kun; Li, Zuo-Yong; Yang, Jing-Yu: Image retrieval based on multi-texton histogram (2010)
  5. Yap, Kim-Hui; Wu, Kui; Zhu, Ce: Knowledge propagation in collaborative tagging for image retrieval (2010)
  6. Zhang, Shile; Li, Bin; Xue, Xiangyang: Semi-automatic dynamic auxiliary-tag-aided image annotation (2010)
  7. Lin, Chia-Chen; Wang, Shing-Shoung: An edge-based image copy detection scheme (2008)
  8. Au, K.M.; Law, N.F.; Siu, W.C.: Unified feature analysis in JPEG and JPEG 2000-compressed domains (2007)
  9. León, T.; Zuccarello, P.; Ayala, G.; de Ves, E.; Domingo, J.: Applying logistic regression to relevance feedback in image retrieval systems (2007)
  10. Park, Sang-Sung; Seo, Kwang-Kyo; Jang, Dong-Sik: Fuzzy art-based image clustering method for content-based image retrieval (2007)
  11. Rajashekhar; Chaudhuri, Subhasis; Namboodiri, Vinay P.: Retrieval of images of man-made structures based on projective invariance (2007)
  12. Wu, Kui; Yap, Kim-Hui: Content-based image retrieval using fuzzy perceptual feedback (2007)
  13. De Ves, E.; Domingo, J.; Ayala, G.; Zuccarello, P.: A novel Bayesian framework for relevance feedback in image content-based retrieval systems (2006)
  14. Kunttu, Iivari; Lepistö, Leena; Rauhamaa, Juhani; Visa, Ari: Fourier-based object description in defect image retrieval (2006)
  15. Laurent, Christophe; Laurent, Nathalie; Maurizot, Mariette; Dorval, Thierry: In depth analysis and evaluation of saliency-based color image indexing methods using wavelet salient features (2006)
  16. Park, Gunhan; Baek, Yunju; Lee, Heung-Kyu: Web image retrieval using majority-based ranking approach (2006)
  17. Lim, Joo-Hwee; Jin, Jesse S.: A structured learning framework for content-based image indexing and visual query (2005)
  18. Nguyen, Giang P.; Worring, Marcel: Relevance feedback based saliency adaptation in CBIR (2005)
  19. Sun, Yongqing; Ozawa, Shinji: HIRBIR: A hierarchical approach to region-based image retrieval (2005)
  20. Chen, Yixin; Li, Jia; Wang, James Z.: Machine learning and statistical modeling approaches to image retrieval. (2004)

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