Title :
A method of type recognition using probabilistic constraint support vector machine
Author_Institution :
Eng. Training, Yantai Univ., Yantai, China
Abstract :
A new support vector machine classifier for recognition of vehicle type which has been captured from traffic scene images. A new support vector machine classifier is presented with probabilistic constrains which presence probability of samples in each class is determined based on a distribution function. Noise is caused to found incorrect support vectors thereupon margin can not be maximized. In the proposed method, constraints boundaries and constraints occurrence have probability density functions which it helps for achieving maximum margin.
Keywords :
image recognition; probability; support vector machines; traffic engineering computing; vehicles; SVM classifier; constraints boundaries; constraints occurrence; distribution function; maximum margin; probabilistic constraint support vector machine; probability density functions; traffic scene images; vehiche type recognition; Feature extraction; Optimization; Pattern recognition; Probabilistic logic; Reliability; Support vector machines; Vehicles; Machine identification; Pattern recognition; Probabilistic constraints; Support vector machine; Vehicle type recognition;
Conference_Titel :
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4673-0198-5
DOI :
10.1109/ICSAI.2012.6223393