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An Efficient Indoor-Outdoor Scene Classification Method  

Kim, Won-Jun (Dept. of Electrical Engineering, KAIST)
Kim, Chang-Ick (Dept. of Electrical Engineering, KAIST)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.46, no.5, 2009 , pp. 48-55 More about this Journal
Abstract
Prior research works in indoor-outdoor classification have been conducted based on a simple combination of low-level features. However, since there are many challenging problems due to the extreme variability of the scene contents, most methods proposed recently tend to combine the low-level features with high-level information such as the presence of trees and sky. To extract these regions from videos, we need to conduct additional tasks, which may yield the increasing number of feature dimensions or computational burden. Therefore, an efficient indoor-outdoor scene classification method is proposed in this paper. First, the video is divided into the five same-sized blocks. Then we define and use the edge and color orientation histogram (ECOH) descriptors to represent each sub-block efficiently. Finally, all ECOH values are simply concatenated to generated the feature vector. To justify the efficiency and robustness of the proposed method, a diverse database of over 1200 videos is evaluated. Moreover, we improve the classification performance by using different weight values determined through the learning process.
Keywords
Indoor-outdoor classification; low-level features; semantic information; ECOH;
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