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A New Method for Indoor-outdoor Image Classification Using Color Correlated Temperature
Ali Nadian Ghomsheh, Alireza Talebpour
Pages - 167 - 181     |    Revised - 15-05-2012     |    Published - 20-06-2012
Volume - 6   Issue - 3    |    Publication Date - June 2012  Table of Contents
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KEYWORDS
Indoor-outdoor Image Classification, Color Segmentation, Color Correlated Temperature
ABSTRACT
In this paper a new method for indoor-outdoor image classification is presented; where the concept of Color Correlated Temperature is used to extract distinguishing features between the two classes. In this process, using Hue color component, each image is segmented into different color channels and color correlated temperature is calculated for each channel. These values are then incorporated to build the image feature vector. Besides color temperature values, the feature vector also holds information about the color formation of the image. In the classification phase, KNN classifier is used to classify images as indoor or outdoor. Two different datasets are used for test purposes; a collection of images gathered from the internet and a second dataset built by frame extraction from different video sequences from one video capturing device. High classification rate, compared to other state of the art methods shows the ability of the proposed method for indoor-outdoor image classification.
CITED BY (4)  
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Mr. Ali Nadian Ghomsheh
- Iran
a_nadian@sbu.ac.ir
Dr. Alireza Talebpour
- Iran