Evaluating a color-based active basis model for object recognition

• Published in: Computer vision and image understanding 2012-11, Vol.116 (11), p.1111-1120
• Main Authors: Quyen Bui, T.T., Hong, Keum-Shik
• Format: Article
• Language: English
• Subjects: Active basis model; Algorithms; Applied sciences; Artificial intelligence; Color-based feature; Computer science; control theory; systems; Computer vision; Deformable template; Exact sciences and technology; Learning; Local power spectrum; Object recognition; Pattern recognition. Digital image processing. Computational geometry; Surface layer; Template matching; Texture
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2012.07.003

► 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. 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.