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Recognizing human gestures using a novel SVM tree

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.