Symbol Recognition Using a 2-class Hierarchical Model of Choquet Integrals

We present an approach allowing to automatically extract a suitable set of soft output classifiers and to aggregate them to provide a global decision using the Choquet integral. This approach relies on two key points. A learning algorithm based on a 2-class model is performed to define a new set of decisions rules assuming to be experts dedicated to recognize one class from another one. All the associated capacities are aggregated again at a high level to recognize symbols. The second is a selection scheme that discards weak or redundant decision rules, keeping only the most relevant subset. An experimental study, based on real world data, is then described. It analyzes the improvements achieve by these points first when used independently, then when combined together.

[1]  M. Sugeno,et al.  A theory of fuzzy measures: Representations, the Choquet integral, and null sets , 1991 .

[2]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[4]  Atul K. Chhabra,et al.  Symbol Recognition : An Overview , 2005 .

[5]  Mario Vento,et al.  Symbol recognition in documents: a collection of techniques? , 2000, International Journal on Document Analysis and Recognition.

[6]  M. Grabisch.  The application of fuzzy integrals in multicriteria decision making , 1996 .

[7]  Ernest Valveny,et al.  Symbol Recognition: Current Advances and Perspectives , 2001, GREC.

[8]  G. Choquet.  Theory of capacities , 1954 .

[9]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Su Yang.  Symbol Recognition via Statistical Integration of Pixel-Level Constraint Histograms: A New Descriptor , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Laurent Wendling,et al.  Binary Shape Normalization Using the Radon Transform , 2003, DGCI.

[13]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Daewon Kim,et al.  Relevance feedback for content-based image retrieval using the Choquet integral , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[15]  Steven Greenberg,et al.  Application of Fuzzy-Integration-Based Multiple-Information Aggregation in Automatic Speech Recognition , 2004 .

[16]  Guojun Lu,et al.  A Comparative Study of Fourier Descriptors for Shape Representation and Retrieval , 2002 .

[17]  Laurent Wendling,et al.  A New Way to Represent the Relative Position between Areal Objects , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Michel Grabisch,et al.  A new algorithm for identifying fuzzy measures and its application to pattern recognition , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[19]  Yasufumi Takama,et al.  Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback , 2005, Signal Process..

[20]  Thomas Bernier,et al.  A new method for representing and matching shapes of natural objects , 2003, Pattern Recognit..

[21]  L. Shapley.  A Value for n-person Games , 1988 .