Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Query by Approximate Shapes Image Retrieval with improved object sketch extraction algorithm

,

DOI: http://dx.doi.org/10.15439/2018F279

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 555559 ()

Full text

Abstract. In this paper a new Content Based Image Retrieval based on a sketch method was proposed. The main idea of the algorithm is based on decomposing an object into predefined set of shapes (primitives): line segments, polylines, polygons, arches, polyarches and arc-sided polygons. All primitives are stored as a graph in order to store the mutual relations between them. Graphs are stored in a tree-based structure which allows fast querying. As an improvement to the algorithm, a conversion to the HSL color space was proposed in order to detect primitive more accurately. Moreover, computing all line slopes in relation to the object oriented bounding box was also proposed. Additionally, in order to better detect objects present in images, the usage of Edge Boxes algorithm was proposed.

References

  1. S. Deniziak and T. Michno, “New content based image retrieval database structure using query by approximate shapes,” in 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Sept 2017. http://dx.doi.org/10.15439/2017F457 pp. 613–621.
  2. H. P. Kriegel, P. Kroger, P. Kunath, and A. Pryakhin, “Effective similarity search in multimedia databases using multiple representations,” in 2006 12th International Multi-Media Modelling Conference, 2006. doi: 10.1109/MMMC.2006.1651355. ISSN 1550-5502 pp. 4
  3. M. Mocofan, I. Ermalai, M. Bucos, M. Onita, and B. Dragulescu, “Supervised tree content based search algorithm for multimedia image databases,” in 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), May 2011. http://dx.doi.org/10.1109/SACI.2011.5873049 pp. 469–472.
  4. C. Y. Li and C. T. Hsu, “Image retrieval with relevance feedback based on graph-theoretic region correspondence estimation,” IEEE Transactions on Multimedia, vol. 10, no. 3, pp. 447–456, April 2008. http://dx.doi.org/10.1109/TMM.2008.917421
  5. S. Parui and A. Mittal, “Sketch-based image retrieval from millions of images under rotation, translation and scale variations,” CoRR, vol. abs/1511.00099, 2015. [Online]. Available: http://arxiv.org/abs/1511. 00099
  6. H. H. Wang, D. Mohamad, and N. A. Ismail, “Approaches, challenges and future direction of image retrieval,” CoRR, vol. abs/1006.4568, 2010.
  7. A. Singh, S. Shekhar, and A. Jalal, “Semantic based image retrieval using multi-agent model by searching and filtering replicated web images,” in Information and Communication Technologies (WICT), 2012 World Congress on, Oct 2012. http://dx.doi.org/10.1109/WICT.2012.6409187 pp. 817–821.
  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, Aug 1992, pp. 530–533.
  9. S. Deniziak and T. Michno, “Content based image retrieval using query by approximate shape,” in 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Sept 2016, pp. 807–816.
  10. C. L. Zitnick and P. Dollár, “Edge boxes: Locating object proposals from edges,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014. ISBN 978-3-319-10602-1 pp. 391–405.
  11. P. Sitek and J. Wikarek, “A hybrid programming framework for modeling and solving constraint satisfaction and optimization problems,” Scientific Programming, vol. 2016, 2016. http://dx.doi.org/10.1155/2016/5102616