PERFORMANCE ANALYSIS OF CHAIN CODE DESCRIPTOR FOR HAND SHAPE CLASSIFICATION

Kshama Fating1 and Archana Ghotkar2 1,2Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India 

ABSTRACT 

Feature Extraction is an important task for any Image processing application. The visual properties of any image are its shape, texture and colour. Out of these shape description plays important role in any image classification. The shape description method classified into two types, contour base and region based. The contour base method concentrated on the shape boundary line and the region based method considers whole area. In this paper, contour based, the chain code description method was experimented for different hand shape. The chain code descriptor of various hand shapes was calculated and tested with different classifier such as k-nearest- neighbour (k-NN), Support vector machine (SVM) and Naive Bayes. Principal component analysis (PCA) was applied after the chain code description. The performance of SVM was found better than k-NN and Naive Bayes with recognition rate 93%. 

KEYWORDS 

Feature extraction, Chain code, k-NN, SVM, Naive Bayes

More Details : http://airccse.org/journal/ijcga/papers/4214ijcga02.pdf