Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
525885 | Computer Vision and Image Understanding | 2012 | 10 Pages |
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.
► A color-based ABM for object recognition is proposed. ► Gabor wavelets of LAB color images and color features are considered. ► The various versions of color-based ABM are explored. ► A significant improvement of the color-based ABM was demonstrated.