Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Optimized discriminative transformations for speech features based on minimum classification error
Pattern Recognition Letters
Polygonal approximation of digital planar curves through vertex betweenness
Information Sciences: an International Journal
Shape retrieval and recognition on mobile devices
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
2D shapes classification using BLAST
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Circular projection for pattern recognition
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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The goal of this paper is to present a weighted likelihood discriminant for minimum error shape classification. Different from traditional maximum likelihood (ML) methods, in which classification is based on probabilities from independent individual class models as is the case for general hidden Markov model (HMM) methods, proposed method utilizes information from all classes to minimize classification error. The proposed approach uses a HMM for shape curvature as its 2-D shape descriptor. We introduce a weighted likelihood discriminant function and present a minimum classification error strategy based on generalized probabilistic descent method. We show comparative results obtained with our approach and classic ML classification with various HMM topologies alongside Fourier descriptor and Zernike moments-based support vector machine classification for a variety of shapes.