Vector Image Retrieval Methods Based on Fuzzy Patterns

Автор: Yevgeniya Sulema, Etienne Kerre, Oksana Shkurat

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 3 vol.12, 2020 года.

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In this work we present two methods of vector graphic objects retrieval based on a fuzzy description of their shapes. Both methods enable the retrieval of vector images resembling to a given fuzzy pattern. The basic method offers a geometrical interpretation of a fuzziness measure as a radius of a circle with the center in each vertex of a given candidate object. It enables the representation of uncertain information about a pattern object defined by its “fuzzy” vertices. The advanced method generalizes this approach by considering an ellipse instead of a circle. The basic method can be used for the comparison of polygons and other primitives in vector images. The advanced method can be used for complex shapes retrieval. To enable saving a “fuzzy” image as a file, the modification of the SVG format with a new attribute “fuzziness” is proposed for both methods. The advanced method practical implementation is illustrated by the retrieval of medical images, namely, heart computer tomography images.

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Information Retrieval, Pattern Recognition, Image Processing, Fuzziness

Короткий адрес: https://sciup.org/15017587

IDR: 15017587   |   DOI: 10.5815/ijmecs.2020.03.02

Список литературы Vector Image Retrieval Methods Based on Fuzzy Patterns

  • Fujita, R. and Hayashi, T. Vector Image Retrieval Based on Approximation of Bezier Curves with Line Segments, in IEEE Pacific Rim Conf. on Communications, Computers and Signal Processing (2011), pp. 431–436.
  • Kaiyuan Jiang et al. Information Retrieval through SVG-based Vector Images Using an Original Method, in IEEE Int. Conf. on e-Business Engineering (2007), pp.183–188.
  • Md. Khalid Imam Rahmani and M. A. Ansari. Fuzzy Image Retrieval: Recent Trends, in Indian Journal of Industrial and Applied Mathematics. 4 (2013), 131–137.
  • Hayashi, T. et al. Retrieval of 2D vector images by matching Weighted Feature Points, in IEEE Int. Conf. on Image Processing (2013).
  • Pu J. and Ramani K. On visual similarity based 2d drawing retrieval, in Journal of Computer Aided Design. 38, 3 (2006), pp. 249–259.
  • Yixin Chen and James Z. Wang. A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval, in IEEE Transactions on Pattern Analysis and Machine Intelligence. 24, 9 (2002), pp. 1–16.
  • Chin-Sheng Chen and Chi-Min Weng. An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System, in Applied Sciences. 7(8) (2017), 1–24.
  • Wang Xiaoling and Xie Kanglin. Application of the Fuzzy Logic in Content-based Image Retrieval, in Journ. of Computer Science Technology. 5 (2005), 19–24.
  • Pedro Martins et al. Clip Art Retrieval Combining Raster and Vector Methods, in the 11th Int. Workshop on Content-Based Multimedia Indexing (2013).
  • Avinash N. Bhute and B. B. Meshram. Content Based Image Indexing and Retrieval, in Int. Journal of Graphics & Image Processing. 3, 4 (2013), pp. 235–246.
  • Minakshi Banerjee Malay K. Kundu. Content Based Image Retrieval with Fuzzy Geometrical Features, in IEEE Int. Conf. on Fuzzy Systems (2003), pp. 932–937.
  • Raghu Krishnapuram et al. Content-based image retrieval based on a fuzzy approach, in IEEE Transactions on Knowledge and Data Engineering (2004), 1185–1199.
  • Yusuke Uchida, Shigeyuki Sakazawa, Shin’ichi Satoh. Image Retrieval with Fisher Vectors of Binary Features, in ITE Transactions on Media Technology and Applications (2016), pp. 1-11.
  • Manuel Fonseca et al. Retrieving Vector Graphics Using Sketches, in Lecture Notes in Computer Science. 3031 (2004), pp. 66–76.
  • Qingyong Li et al. Image Retrieval Based on Fuzzy Color Semantics, in IEEE Int. Conf. on Fuzzy Syst. (2007), 1–5.
  • Scalable Vector Graphics (SVG), available at: http://www.w3.org/TR/SVG11
  • Extensible Markup Language (XML), available at: http://www.w3.org/TR/REC-xml
  • De Cock, M. and Kerre, E. On (un)suitable fuzzy relations to model approximate equality. Fuzzy Sets and Systems, 133 (2003), pp. 137–153.
  • De Cock, M. and Kerre, E. Why fuzzy T-equivalence relations do not resolve the Poincaré paradox, and related issues, in Fuzzy Sets and Systems, 133 (2003), 181–192.
  • Shkurat O., Sulema Y., Dychka A. Complicated Shapes Estimation Method for Objects Analysis in Video Surveillance System, KPI Science News, 3 (2018), 53–62.
  • Karamti H., Tmar M., Gargouri F. Vectorization of Content-based Image Retrieval Process Using Neural Network, in 16th Int. Conf. on Enterprise Information Systems (2014), pp. 435–439.
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