Logo recognition based on membership degree and closeness degree of fuzzy sets

Fuzzy information theory which is based on fuzzy theory is a departure from information science. Higher performance could be obtained in pattern recognition with fuzzy sets theory. People combine fuzzy sets theory with pattern recognition and established fuzzy pattern recognition. This paper presents our work in the field of logo recognition by using fuzzy sets theory. Aim at the application requirement of logo recognition in image intelligent processing, a logo recognition algorithm based on membership degree and closeness degree of fuzzy sets is proposed. By fuzzy mapping, the logo's gridding feature is transformed to the membership degree of fuzzy sets; then using closeness degree and closest principle of fuzzy sets to accomplish logo recognition. The algorithm significantly enhances the adaptability and anti-interference to poor quality images and effectively improves the flexible processing capability of logo recognition system. Experimental results show that the recognition rate of this logo recognition algorithm can reach as high as 94.5%.

[1]  Edward T. Lee.  An application of fuzzy sets to the classification of geometric figures and chromosome images , 1976, Inf. Sci..

[2]  Mysore Y. Jaisimha.  Wavelet features for similarity-based retrieval of logo images , 1996, Electronic Imaging.

[3]  Arun K. Majumdar,et al.  A supervised learning algorithm for hierarchical classification of fuzzy patterns , 1983, Inf. Sci..

[4]  Richard Price,et al.  A generalized regression neural network for logo recognition , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[5]  Ehud Rivlin,et al.  Logo recognition using geometric invariants , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[6]  Francesca Cesarini,et al.  A neural-based architecture for spot-noisy logo recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[7]  Kheireddine Chafaa,et al.  Indirect adaptive interval type-2 fuzzy control for nonlinear systems , 2007, Int. J. Model. Identif. Control..

[8]  Nicolas Langlois,et al.  Fuzzy control of a turbocharged diesel engine , 2008, Int. J. Model. Identif. Control..

[9]  Ping Xi-jian.  Logo recognition and retrieval algorithm in document image , 2006 .