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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Content Based Image Retrieval using Query by Approximate Shape


DOI: http://dx.doi.org/10.15439/2016F233

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 807816 ()

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Abstract. In this paper we present a new method for content-based image retrieval. The method is based on querying database by approximate shape representing given object. In this way all images containing the object may be found. Shapes are specified as a set of geometric primitives and attributes. Relations between primitives are represented by a graph. Our graph matching algorithm is used for computing the level of similarity between shapes. The method may be also used for searching transformed as well as partially covered objects. Experimental results showed the efficiency of our approach.


  1. H. H. Wang, D. Mohamad, and N. A. Ismail, “Approaches, challenges and future direction of image retrieval” Journal of Computing, vol. 2, No.6, 2010, pp. 193-199
  2. R. Datta, D. Joshi, J. Li, J. Z. Wang “Image Retrieval: Ideas, Influences, and Trends of the New Age.” ACM Computing Surveys, 40, 2, 2008, 5:1–5:60, http://dx.doi.org/10.1145/1348246.1348248
  3. M. Mocofan, I. Ermalai, M. Bucos, M. Onita, and B. Dragulescu, “Supervised tree content based search algorithm for multimedia image databases”, 6th IEEE International Symposium on Applied Computational Intelligence and Informatics, 2011, pp. 469–472, doi: 10.1109/SACI.2011.5873049
  4. T. K. Shih, “Distributed multimedia databases” T. K. Shih, Ed. Hershey, PA, USA: IGI Global, ch. Distributed Multimedia Databases, 2002, pp. 2–12
  5. H.-P. Kriegel, P. Kroger, P. Kunath, and A. Pryakhin, “Effective similarity search in multimedia databases using multiple representations” in 12th International Multi-Media Modelling Conference Proceedings, 2006, pp. 389–392, http://dx.doi.org/10.1109/MMMC.2006.1651355
  6. C. Lalos, A. Doulamis, K. Konstanteli, P. Dellias, and T. Varvarigou, “An innovative content-based indexing technique with linear response suitable for pervasive environments” in International Workshop on Content-Based Multimedia Indexing, 2008, pp. 462–469, http://dx.doi.org/10.1109/CBMI.2008.4564983
  7. M. Bielecka and M. Skomorowski, “Fuzzy-aided parsing for pattern recognition” in Computer Recognition Systems 2, ser. Advances in Soft Computing, M. Kurzynski, E. Puchala, M. Wozniak, and A. Zolnierek, Eds. Springer Berlin Heidelberg, vol. 45, 2007, pp. 313–318, http://dx.doi.org/10.1007/978-3-540-75175-5_39
  8. T. Kato, T. Kurita, N. Otsu, and K. Hirata, “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, 1992, pp. 530–533, http://dx.doi.org/10.1109/ICPR.1992.201616
  9. J. F. Nunes, P. M. Moreira and J. M. R. S. Tavares, “Shape based image retrieval and classification”, 5th Iberian Conference on Information Systems and Technologies (CISTI), 2010
  10. D. Zhang, G. Lu “Shape-based image retrieval using generic Fourier descriptor” Signal Processing: Image Communication. 17, 10, 2002, pp. 825–848
  11. C. E. Jacobs, A. Finkelstein, D.H. Salesin “Fast Multiresolution Image Querying” Proc. of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, 1995, pp. 277–286
  12. S. Deniziak, T. Michno “Query by Shape for Image Retrieval from Multimedia Databases.” Communications in Computer and Information Science, Springer, 521, 2015, pp. 377–386, http://dx.doi.org/10.1007/978-3-319-18422-7_33
  13. S. Deniziak, T. Michno “Query-by-Shape Interface for Content Based Image Retrieval” 8th IEEE International Conference on Human System Interactions (HSI), 2015, pp. 108–114, http://dx.doi.org/10.1109/HSI.2015.7170652
  14. S. Deniziak, T. Michno, A. Krechowicz “The Scalable Distributed Two-layer Content Based Image Retrieval Data Store” 8th International Symposium on Multimedia Applications and Processing, Federated Conference on Computer Science and Information Systems (FedCSIS), 2015, pp. 827–832, http://dx.doi.org/10.15439/2015F272
  15. Chao Ma, Xiaokang Yang, Chongyang Zhang, Xiang Ruan, and Ming-Hsuan Yang, “Sketch Retrieval via Local Dense Stroke Features” Image and Vision Computing (IVC), 2016, http://dx.doi.org/10.1016/j.imavis.2015.11.007
  16. R. Krishnamoorthy, , S. Sathiya Devi, “Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model” Digital Signal Processing, Volume 23, Issue 2, March 2013, pp. 555–568 , http://dx.doi.org/10.1016/j.dsp.2012.09.018
  17. A. S. Mouratoa, R. Jesus, “Clip art retrieval using a sketch. Tablet application.” Conference on Electronics, Telecommunications and Computers – CETC 2013, http://dx.doi.org/10.1016/j.protcy.2014.10.246
  18. R. Grompone von Gioi, J. Jakubowicz, J.-M. Morel, G. Randall, “LSD: a Line Segment Detector”, Image Processing On Line, 2 (2012), pp. 35–55, http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd