Beyond shape
Jun Zhang, Zhao Xie, Jun Gao, Kewei Wu · 2010
Being lack of theoretical support from biological cues in computer vision, current computational and learning approaches of object categorization mostly aim at better performances neglecting analysis on framework in human brain for visual information processing materially which cause little-marginal improvement and more complexity. Focusing on the uncertainty of color mechanism in visual cortex and motivating from biological issues on shape information, we present the model incorporating color invariant descriptors and plausible shape feature biologically to formulate the robust representation of each category with only simple SVM classifier to achieve the amazing performance. Our model has the characteristics of illumination, scale, position, orientation, viewpoint invariance, and competitive with current algorithms on only a few training examples from several data sets, including Caltech 101 and GRAZ for category recognition. Also, experimental results show the robustness when challenged by noisy or blurred images.