WITHDRAWN: Video event classification using KNN classifier with hybrid features

Abstract

Event detection from videos has become a demanding and challenging subject with the creation of huge video data. Detection of complex interesting events automatically in user generated videos is a difficult job because of its features that differ from video broadcasting. We present an effective technique for detecting actions or events in UCF 101 video large scale data. Histogram equalization is initially used on UCF101 database for contrast enhancement in the pre processing stage. In the later stage gradient local ternary pattern, histogram of oriented gradient and tamuras features are extracted from the enhanced images to accomplish feature extraction. Finally k nearest neighbor (KNN) classifier is used to classify the events. In this work we compared Random Forest classifier with KNN classifier in terms of accuracy, specificity, sensitivity, false discovery rate, false omission rate and error rate. KNN gave 90.25% accuracy where as Random forest classifier yielded 77.18% accuracy. The result exhibits that KNN classifier gives better performance than RF classifier.