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Proceedings Paper

Recognizing human gestures using a novel SVM tree
Author(s): Hitesh Jain; Abhik Chatterjee; Sanjeev Kumar; Balasubramanian Raman
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Paper Abstract

In this paper, a novel support vector machine (SVM) tree is proposed for gesture recognition from the silhouette images. A skeleton based strategy is adopted to extract the features from a video sequence representing any human gesture. In our binary tree implementation of SVM, the number of binary classifiers required is reduced since, instead of grouping different classes together in order to train a global classifier, we select two classes for training at every node of the tree and use probability theory to classify the remaining points based on their similarities and differences to the two classes used for training. This process is carried on, randomly selecting two classes for training at a node, thus creating two child nodes and subsequently assigning the classes to the nodes derived. In the classification phase, we start out at the root node. At each node of the tree, a binary decision is made regarding the assignment of the input data point to either of the group represented by the left and right sub-tree of the node which may contain multiple classes. This is repeated recursively downward until we reach a leaf node that represents the class to which the input data point belonging. Finally, the proposed framework is tested on various data sets to check its efficiency. Encouraging results are achieved in terms of classification accuracy.

Paper Details

Date Published: 2 February 2012
PDF: 9 pages
Proc. SPIE 8300, Image Processing: Machine Vision Applications V, 83000M (2 February 2012); doi: 10.1117/12.908087
Show Author Affiliations
Hitesh Jain, Indian Institute of Technology Roorkee (India)
Abhik Chatterjee, Indian Institute of Technology Roorkee (India)
Sanjeev Kumar, Indian Institute of Technology Roorkee (India)
Balasubramanian Raman, Indian Institute of Technology Roorkee (India)

Published in SPIE Proceedings Vol. 8300:
Image Processing: Machine Vision Applications V
Philip R. Bingham; Edmund Y. Lam, Editor(s)

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