Jaeho Lee, Hyunkyung Shin, Journal of Internet Computing and Services, Vol. 15, No. 6, pp. 117-124, Dec. 2014
10.7472/jksii.2014.15.6.117, Full Text:
Keywords: feature vector selection, nearest neighbor search, Principal Component Analysis, salient feature detection, Machine Learning

Abstract

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naive Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

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