Object Recognition by Using Multi-level Feature Point Extraction
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In this paper, we present a novel approach for object recognition in real-time by employing multilevel feature analysis and demonstrate the practicality of adapting feature extraction into a Naive Bayesian classification framework that enables simple, efficient, and robust performance. We also show the proposed method scales well as the number of level-classes grows. To effectively understand the patches surrounding a keypoint, the trained classifier uses hundreds of simple binary features and models class posterior probabilities. In addition, the classification process is computationally cheap under the assumed independence between arbitrary sets of features. Even though for some particular scenarios, this assumption can be invalid. We demonstrate that the efficient classifier nevertheless performs remarkably well on image datasets with a large variation in the illumination environment and image capture perspectives. The experiment results show consistent accuracy can be achieved on many challenging dataset while offer interactive speed for large resolution images. The method demonstrates promising results that outperform the state-of-the-art methods on pattern recognition.
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