International Journal of Modern Education and Computer Science (IJMECS)
ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)
Published By: MECS Press
IJMECS Vol.12, No.3, Jun. 2020
Vector Image Retrieval Methods Based on Fuzzy Patterns
Full Text (PDF, 826KB), PP.8-16
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.
Cite This Paper
Yevgeniya Sulema, Etienne Kerre, Oksana Shkurat, " Vector Image Retrieval Methods Based on Fuzzy Patterns", International Journal of Modern Education and Computer Science(IJMECS), Vol.12, No.3, pp. 8-16, 2020.DOI: 10.5815/ijmecs.2020.03.02
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.