Abstract: Efficient methods of image retrieval is one of the most important challenges in the scope of the management of large multimedia databases. Existing methods for querying, based on a textual description e.g. keywords or based on image content, are not sufficient for the most applications. Methods based on semantic features are more suitable. In this paper we propose a new query by shape (QS) method for image retrieval from multimedia databases. Each image in the database is represented as a set of graphical objects, which are specified using graphical primitives like lines, circles, polygons etc. To retrieve images containing the given object, the object shape should be provided. Next, the efficient algorithm for testing the similarity of shapes is applied. The preliminary results showed the high effectiveness of the QS method.
B I B L I O G R A F I A1. Aggarwal, G., Ashwin, T., Ghosal, S.: An image retrieval system with automatic
query modification. IEEE Transactions on Multimedia 4(2), 201–214 (Jun 2002)
2. Bielecka, M., Skomorowski, M.: Fuzzy-aided parsing for pattern recognition. In:
Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds.) Computer Recogni-
tion Systems 2, Advances in Soft Computing, vol. 45, pp. 313–318. Springer Berlin
Heidelberg (2007)
3. Daoudi, M., Matusiak, S.: Visual image retrieval by multiscale description of user
sketches. J. Vis. Lang. Comput. 11(3), 287–301 (2000)
4. Del Bimbo, A., Pala, P.: Visual image retrieval by elastic matching of user sketches.
IEEE Trans. on Pattern Analysis and Machine Intelligence 19(2), 121–132 (Feb
1997)
5. Jakubowski, R.: Extraction of shape features for syntactic recognition of mechan-
ical parts. IEEE Trans. on Systems, Man and Cybernetics SMC-15(5), 642–651
(Sept 1985)
6. Kato, T., Kurita, T., Otsu, N., Hirata, K.: A sketch retrieval method for full color
image database-query by visual example. In: 11th IAPR International Conference
on Pattern Recognition, Vol.I. Conference A: Computer Vision and Applications.
pp. 530–533 (Aug 1992)
7. Kriegel, H.P., Kroger, P., Kunath, P., Pryakhin, A.: Effective similarity search in
multimedia databases using multiple representations. In: 12th International Multi-
Media Modelling Conference Proceedings. pp. 4 pp.– (2006)
8. Lalos, C., Doulamis, A., Konstanteli, K., Dellias, P., Varvarigou, T.: An innovative
content-based indexing technique with linear response suitable for pervasive envi-
ronments. In: International Workshop on Content-Based Multimedia Indexing. pp.
462–469 (June 2008)
9. Lee, H.C., Fu, K.S.: Generating object descriptions for model retrieval. IEEE Trans.
on Pattern Analysis and Machine Intelligence PAMI-5(5), 462–471 (Sept 1983)
10. Li, C.Y., Hsu, C.T.: Image retrieval with relevance feedback based on graph-
theoretic region correspondence estimation. IEEE Transactions on Multimedia
10(3), 447–456 (April 2008)
11. Lukawski, G., Sapiecha, K.: Balancing workloads of servers maintaining scalable
distributed data structures. In: 19th Euromicro International Conference on Par-
allel, Distributed and Network-Based Processing. pp. 80–84 (Feb 2011)
12. Mocofan, M., Ermalai, I., Bucos, M., Onita, M., Dragulescu, B.: Supervised tree
content based search algorithm for multimedia image databases. In: 6th IEEE
International Symposium on Applied Computational Intelligence and Informatics.
pp. 469–472 (May 2011)
13. Shih, T.K.: Distributed multimedia databases. chap. Distributed Multimedia
Databases, pp. 2–12. IGI Global, Hershey, PA, USA (2002)
14. Sitek, P., Wikarek, J.: A hybrid framework for the modelling and optimisation of
decision problems in sustainable supply chain management. International Journal
of Production Research (2015)
15. Sluzek, A.: On moment-based local operators for detecting image patterns. Image
and Vision Computing 23(3), 287 – 298 (2005)
16. Wang, H.H., Mohamad, D., Ismail, N.A.: Approaches, challenges and future direc-
tion of image retrieval. CoRR abs/1006.4568 (2010) 
Pełny tekst 
DOI 
Web of Science