Place Modeling and Recognition using Distribution of Scale Invariant Features
2008, 13(4), pp.51-58
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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In this paper, we propose a place modeling based on the distribution of scale-invariant features, and a place recognition method that recognizes places by comparing the place model in a database with the extracted features from input data. The proposed method is based on the assumption that every place can be represented by unique feature distributions that are distinguishable from others. The proposed method uses global information of each place where one place is represented by one distribution model. Therefore, the main contribution of the proposed method is that the time cost corresponding to the increase of the number of places grows linearly without increasing exponentially. For the performance evaluation of the proposed method, the different number of frames and the different number of features are used, respectively. Empirical results illustrate that our approach achieves better performance in space and time cost comparing to other approaches. We expect that the proposed method is applicable to many ubiquitous systems such as robot navigation, vision system for blind people, wearable computing, and so on.
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