Noisy logo recognition using line segment Hausdorff distance

Logo recognition is of great interest in the document and shape analysis domain. In order to develop a recognition method that is robust to employ under adverse conditions such as different scale/orientation, broken curves, added noise and occlusion, a modified line segment Hausdorff distance is proposed in this paper. The new approach has the advantage to incorporate structural and spatial information to compute dissimilarity between two sets of line segments rather than two sets of points. The proposed technique has been applied on line segments generated from logos with encouraging results. Clear cut distinction between the correct and incorrect matches has been observed. This suggests a strong potential for logo and shape recognition system.

[1]  Raimondo Schettini,et al.  Content-based similarity retrieval of trademarks using relevance feedback , 2001, Pattern Recognit..

[2]  Benoit Huet,et al.  Line Pattern Retrieval Using Relational Histograms , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Whoi-Yul Kim,et al.  Content-based trademark retrieval system using a visually salient feature , 1998, Image Vis. Comput..

[4]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Hanan Samet,et al.  Using negative shape features for logo similarity matching , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[6]  Nicu Sebe,et al.  Shape Based Retrieval , 2003 .

[7]  Shu-Yuan Chen,et al.  Trademark shape recognition using closed contours , 1997, Pattern Recognit. Lett..

[8]  Yong Sheng. Gao.  Human face recognition using line edge information , 2000 .

[9]  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.

[10]  David Zhang,et al.  Two novel characteristics in palmprint verification: datum point invariance and line feature matching , 1999, Pattern Recognit..

[11]  Ehud Rivlin,et al.  Applying algebraic and differential invariants for logo recognition , 1996, Machine Vision and Applications.

[12]  Anil K. Jain,et al.  Shape-Based Retrieval: A Case Study With Trademark Image Databases , 1998, Pattern Recognit..

[13]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[14]  Yee-Hong Yang,et al.  Dynamic two-strip algorithm in curve fitting , 1990, Pattern Recognit..

[15]  Olivier Y. de Vel,et al.  Line-Based Face Recognition under Varying Pose , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Mohan S. Kankanhalli,et al.  Shape Measures for Content Based Image Retrieval: A Comparison , 1997, Inf. Process. Manag..

[17]  Gian Antonio Mian,et al.  Trademark shapes description by string-matching techniques , 1994, Pattern Recognit..

[18]  Anil K. Jain,et al.  A Modiied Hausdorr Distance for Object Matching , 1994 .

[19]  Ehud Rivlin,et al.  Applying algebraic and differential invariants for logo recognition , 1996 .

[20]  William Rucklidge,et al.  Efficiently Locating Objects Using the Hausdorff Distance , 1997, International Journal of Computer Vision.

[21]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[22]  Francesca Cesarini,et al.  A Hybrid System for Locating and Recognizing Low Level Graphic Items , 1995, GREC.