Multiclass classification based on clustering approaches for obstacle recognition in traffic scenes

Traffic scene object detection and recognition is extensively researched in the field of roadside assistance. Due to its importance, many methods have been proposed to solve the classification of objects in traffic and aim classification in different lighting conditions, scaling, orientation and shape of objects. Although most methods for classification are binary classification, often need multiclass classification to reduce the computational effort and especially for traffic are several items that need to be detected and classified. In this paper are tested several methods for multiclass classification.

[1]  Jason Weston,et al.  Support vector machines for multi-class pattern recognition , 1999, ESANN.

[2]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[3]  Eyke Hüllermeier,et al.  Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting , 2010, Pattern Recognit..

[4]  Peter Clark,et al.  Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.

[5]  B. Fei,et al.  Binary tree of SVM: a new fast multiclass training and classification algorithm , 2006, IEEE Transactions on Neural Networks.

[6]  Sung-Bae Cho,et al.  Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers , 2008, Pattern Recognit..

[7]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[8]  Jennifer G. Dy,et al.  A hierarchical method for multi-class support vector machines , 2004, ICML.

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  Francisco Herrera,et al.  Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations , 2010, Fuzzy Sets Syst..

[11]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[12]  Eric C. C. Tsang,et al.  Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification , 2008, IEEE Transactions on Neural Networks.

[13]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[14]  Joydeep Ghosh,et al.  Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis , 2002, Pattern Analysis & Applications.

[15]  Leandro Nunes de Castro,et al.  The proposal of a fuzzy clustering algorithm based on particle swarm , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[16]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[17]  Emilio Corchado,et al.  A survey of multiple classifier systems as hybrid systems , 2014, Inf. Fusion.

[18]  Eyke Hüllermeier,et al.  FR3: A Fuzzy Rule Learner for Inducing Reliable Classifiers , 2009, IEEE Transactions on Fuzzy Systems.

[19]  Johannes Fürnkranz,et al.  Round Robin Classification , 2002, J. Mach. Learn. Res..

[20]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.