Evaluating the Effectiveness and the Efficiency of a Vector Image Search Tool

1. Chim, Y.C., Kassim, A.A., Ibrahim, Y.: Character recognition using statistical moments. Image and Vision Computing 17, 299–307 (1997)

   Article  Google Scholar 

2. de Vries, A.P.: The role of evaluation in the development of content-based retrieval techniques

   Google Scholar 

3. Eakins, J.P., Boardman, J.M., Graham, M.E.: Similarity retrieval of trademark images. Multimedia, IEEE 5(2), 53–63 (1998)

   Article  Google Scholar 

4. Heesch, D., Ruger, S.: Combining features for content-based sketch retrieval: a comparative evaluation of retrieval performance. IEEE Transaction on Image Processing

   Google Scholar 

5. Hu, M.K.: Visual pattern recognition by moments invariants. IRE Transactions on Information Theory 8, 179–187 (1962)

   MATH  Google Scholar 

6. Huang, T., Mehrotra, S., Ramchandran, K.: Multimedia analysis and retrieval system (MARS) project. In: Data Processing Clinic (1996)

   Google Scholar 

7. Koskela, M., Laasonen, J., Laakso, S., Oja, E.: Evaluating the performance of content based imag retrieval system. In: Laurini, R. (ed.) VISUAL 2000. LNCS, vol. 1929, pp. 430–441. Springer, Heidelberg (2000)

   Chapter  Google Scholar 

8. Liu, W., Su, Z., Li, S., Zhang, H.: A performance evaluation protocol for content-based image retrieval algorithms/systems (2001)

   Google Scholar 

9. Di Mascio, T., Francesconi, M., Frigioni, D., Tarantino, L.: Tuning a CBIR system for vector images: The interface support. In: Proceedings of Working Conference on Advanced Visual Interfaces (AVI 2004), pp. 425–428. ACM, New York (2004)

   Chapter  Google Scholar 

10. Di Mascio, T., Frigioni, D., Tarantino, L.: Evaluation of VISTO: the new vector image search tool. In: Jacko, J.A. (ed.) HCI 2007. LNCS, vol. 4552, pp. 836–845. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

11. Di Mascio, T., Frigioni, D., Tarantino, L.: A visual environment for tuning content-based vector image retrieval. In: Lawrence Erlbaum Associates (ed.) Proceedings of HCI 2005, Adjunctive Proceedings (2005)

    Google Scholar 

12. Niblack, W., Barber, R.: The qbic project: Querying images by content using color, texture and shape. In: Proceedings of Conference on Storage and Retrieval for Image and Video Databases, pp. 173–187 (1993)

    Google Scholar 

13. Ogle, V.E., Stonebraker, M.: Chabot: Retrieval from a relational database of images. IEEE Computer 28(9), 40–48 (1995)

    Article  Google Scholar 

14. Smith, J.R., Chang, S.-F.: Visualseek: A fully automated content-based image query system. ACM Multimedia, 87–98 (1996)

    Google Scholar 

15. Smith, J.R.: Image retrieval evaluation. In: IEEE WorkShop on Content-Based Access of Image and Video Libreries (June 1998)

    Google Scholar 

16. Veltkamp, R.C., Tanase, M.: A survey of content-based image retrieval systems. In: Proc. of Content-based image and video retrieval, pp. 47–101. Kluwer Academic Publishers, Dordrecht (2002)

    Chapter  Google Scholar 

17. Yang, L., Albregtsen, F.: Fast computation of invariant geometric moments: a new method giving correct results. In: Proceedings of IEEE International Conference on Pattern Recognition, pp. 201–204 (1994)

    Google Scholar 

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