Evaluating a color-based active basis model for object recognition |
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Authors: | T.T. Quyen Bui Keum-Shik Hong |
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Affiliation: | 1. School of Mechanical Engineering, Pusan National University, 30 Jangjeon-dong, Gumjeong-gu, Busan 609-735, Republic of Korea;2. Department of Cogno-Mechatronics Engineering and School of Mechanical Engineering, Pusan National University, 30 Jangjeon-dong, Gumjeong-gu, Busan 609-735, Republic of Korea |
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Abstract: | Wu and coworkers introduced an active basis model (ABM) for object recognition in 2010, in which the learning algorithm tends to sketch edges in textures. A grey-value local power spectrum was used to find a common template and deformable templates from a set of training images and to detect an object in new images by template matching. In this paper, we propose a color-based active basis model (color-based ABM for short), which incorporates color information. We adopt the framework of Wu et al. in the learning, detection, and classification of the color-based ABM. However, in order to improve the performance in object recognition, we modify the framework of Wu et al. by using different color-based features in both the learning and template matching algorithms. In this color-based ABM approach, two types of learning (i.e., supervised learning and unsupervised learning) are also explored. Moreover, the usefulness of the color-based ABM for practical object recognition in computer vision applications is demonstrated and its significant improvement in recognizing objects is reported. |
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Keywords: | Object recognition Active basis model Local power spectrum Deformable template Color-based feature |
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