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http://dx.doi.org/10.12673/jant.2011.15.1.140

Fast Object Classification Using Texture and Color Information for Video Surveillance Applications  

Islam, Mohammad Khairul (Dept. of Information & Telecommunication Engineering, Korea Aerospace University)
Jahan, Farah (Dept. of Information & Telecommunication Engineering, Korea Aerospace University)
Min, Jae-Hong (Dept. of Information & Telecommunication Engineering, Korea Aerospace University)
Baek, Joong-Hwan (Dept. of Information & Telecommunication Engineering, Korea Aerospace University)
Publication Information
Journal of Advanced Navigation Technology / v.15, no.1, 2011 , pp. 140-146 More about this Journal
Abstract
In this paper, we propose a fast object classification method based on texture and color information for video surveillance. We take the advantage of local patches by extracting SURF and color histogram from images. SURF gives intensity content information and color information strengthens distinctiveness by providing links to patch content. We achieve the advantages of fast computation of SURF as well as color cues of objects. We use Bag of Word models to generate global descriptors of a region of interest (ROI) or an image using the local features, and Na$\ddot{i}$ve Bayes model for classifying the global descriptor. In this paper, we also investigate discriminative descriptor named Scale Invariant Feature Transform (SIFT). Our experiment result for 4 classes of the objects shows 95.75% of classification rate.
Keywords
SURF; SIFT; Color Histogram; Bag of Words; K-Means; Na$\ddot{i}$ve Bayes;
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