Skip to main content


Synobins: An Intermediate Level towards Annotation and Semantic Retrieval

Article metrics

  • 998 Accesses

  • 1 Citations


To reason about the meaning of an image, useful information should be provided with that image; however, images often contain little to no textual information about the objects they are depicting, which is the precise reason why there is a need for CBIR systems that exploit only the correlations present in the raw pixel data. In this paper, we proposed a new type of image feature, which consists of patterns of colors and intensities that capture the latent associations among images and primitive features in such a way that the noise and redundancy are eliminated. We introduced the synobin, a new term for content-based image retrieval literature, which is the equivalent of a synonym word from text retrieval, to name the bin that is synonymous with other bins of a color feature, in the sense that they are similarly used across the image database. In a formal definition, a group of synobins is given by the most important bins participating in forming of a useful pattern, that is, the bins having the highest coefficients in the linear combination defining that pattern. Incorporating our feature model into a CBIR system moves the research in image retrieval beyond simple matching of images based on their primitive features and creates a ground for learning image semantics from visual content. A system developed using our proposed feature model will have the capability of learning associations not only between semantic concepts and images, but also between semantic concepts and patterns. We evaluated the performance of our system based on the retrieval accuracy and on the perceptual similarity order among retrieved images. When compared to standard image retrieval methods, our preliminary results show that even if the feature space was reduced to only 3%–5% of the initial space, the accuracy and perceptual similarity for our system remain the same or better depending on the category of images.


  1. 1.

    Santini S, Jain R: Similarity is a geometer. Multimedia Tools and Applications 1997, 5(3):277–306. 10.1023/A:1009651725256

  2. 2.

    Guttman A: R-trees: a dynamic index structure for spatial searching. Proceedings of ACM SIGMOD International Conference on the Management of Data (SIGMOD '84), June 1984, Boston, Mass, USA 47–57.

  3. 3.

    Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R: Indexing by latent semantic analysis. Journal of American Society for Information Science 1990, 41(6):391–407. 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

  4. 4.

    Flickner M, Sawhney H, Niblack W, et al.: Query by image and video content: the QBIC system. IEEE Computer 1995, 28(9):23–32. 10.1109/2.410146

  5. 5.

    Bach JR, Fuller C, Gupta A, et al.: Virage image search engine: an open framework for image management. Storage and Retrieval for Still Image and Video Databases IV, February 1996, San Jose, Calif, USA, Proceedings of SPIE 2670: 76–87.

  6. 6.

    Feder J: Towards image content-based retrieval for the world-wide web. Advanced Imaging 1996, 11(1):26–29.

  7. 7.

    Smith JR, Chang S-F: VisualSEEk: a fully automated content-based image query system. Proceedings of 4th ACM International Multimedia Conference (ACM Multimedia '96), November 1996, Boston, Mass, USA 87–98.

  8. 8.

    Ortega M, Rui Y, Chakrabarti K, Mehrotra S, Huang TS: Supporting similarity queries in MARS. Proceedings of 5th ACM International Multimedia Conference (ACM Multimedia '97), November 1997, Seattle, Wash, USA 403–413.

  9. 9.

    Ma WY, Manjunath BS: NeTra: a toolbox for navigating large image databases. Proceedings of IEEE International Conference on Image Processing (ICIP '97), October 1997, Santa Barbara, Calif, USA 1: 568–571.

  10. 10.

    Eakins JP, Graham ME: Content-based image retrieval: a report to the JISC technology applications programme. Institute for Image Data Research, University of Northumbria, Newcastle, Tyneside, UK; 1999.

  11. 11.

    La Cascia M, Sethi S, Sclaroff S: Combining textual and visual cues for content-based image retrieval on the World Wide Web. Proceedings of IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '98), June 1998, Santa Barbara, Calif, USA 24–28.

  12. 12.

    Westerveld T, Hiemstra D, de Jong F: Extracting bimodal representations for language-based image retrieval. Proceedings of Multimedia '99, Proceedings of the Eurographics Workshop, September 1999, Milano, Italy 33–42.

  13. 13.

    Petrou M, Bosdogianni P: Image Processing: The Fundamentals. John Wiley & Sons, New York, NY, USA; 1999.

  14. 14.

    Zhiu L, Rao A, Zhang A: Theory of keyblock-based image retrieval. ACM Transactions on Information Systems 2002, 20(2):224–257. 10.1145/506309.506313

  15. 15.

    Zhu L, Rao A, Zhang A: Advanced feature extraction for keyblock-based image retrieval. Information Systems 2002, 27(8):537–557. 10.1016/S0306-4379(02)00020-0

  16. 16.

    Minka TP, Picard RW: Interactive learning using a society of models. In Tech. Rep. 349. MIT Media Laboratory Perceptual Computing Section, Cambridge, Mass, USA; 1995.

  17. 17.

    Niblack CW, Barber R, Equitz W, et al.: QBIC project: querying images by content, using color, texture, and shape. Storage and Retrieval for Image and Video Databases, January–February 1993, San Jose, Calif, USA, Proceedings of SPIE 1908: 173–187.

  18. 18.

    Stan D, Sethi IK: Color patterns for pictorial content description. Proceedings of ACM Symposium on Applied Computing (SAC '02), March 2002, Madrid, Spain 693–698.

  19. 19.

    Swain MJ, Ballard DH: Color indexing. International Journal of Computer Vision 1991, 7(1):11–32. 10.1007/BF00130487

  20. 20.

    Sethi IK, Coman IL, Day B, et al.: Color-WISE: a system for image similarity retrieval using color. Storage and Retrieval for Image and Video Databases VI, January 1998, San Jose, Calif, USA, Proceedings of SPIE 3312: 140–149.

  21. 21.

    Baezo-Yates R, Ribeiro-Neto B: Modern Information Retrieval. Addison-Wesley-Longman, Boston, Mass, USA; 1999.

  22. 22.

    Trefethen LN, Bau D III: Numerical Linear Algebra. SIAM, Philadelphia, Pa, USA; 1997.

  23. 23.

    Rui Y, Huang TS, Chang S-F: Image retrieval: past, present, and future. Journal of Visual Communication and Image Representation 1999, 10: 1–23. 10.1006/jvci.1998.0408

  24. 24.

    White DA, Jain R: Similarity indexing: algorithms and performance. Storage and Retrieval for Still Image and Video Databases IV, February 1996, San Jose, Calif, USA, Proceedings of SPIE 2670: 62–73.

  25. 25.

    Stan D, Sethi IK: Image retrieval using a hierarchy of clusters. Proceedings of 2nd International Conference on Advances in Pattern Recognition (ICAPR '01), March 2001, Rio de Janeiro, Brazil 377–386.

  26. 26.

    Stan D, Sethi IK: Mapping low-level image features to semantic concepts. Storage and Retrieval for Media Databases, January 2001, San Jose, Calif, USA, Proceedings of SPIE 4315: 172–179.

  27. 27.

    Berry MW, Drmac Z, Jessup ER: Matrices, vector spaces, and information retrieval. SIAM Review 1999, 41(2):335–362. 10.1137/S0036144598347035

Download references

Author information

Correspondence to Daniela Stan Raicu.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Raicu, D.S., Sethi, I.K. Synobins: An Intermediate Level towards Annotation and Semantic Retrieval. EURASIP J. Adv. Signal Process. 2006, 063124 (2006) doi:10.1155/ASP/2006/63124

Download citation


  • Image Retrieval
  • Semantic Concept
  • Visual Content
  • Perceptual Similarity
  • Retrieval Accuracy