Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Robust photometric invariant features from the color tensor
IEEE Transactions on Image Processing
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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.