International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Biologically Inspired Features for Face Processing
International Journal of Computer Vision
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Handwritten-Word Spotting Using Biologically Inspired Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biologically inspired feature manifold for gait recognition
Neurocomputing
Kernel-based metric learning for semi-supervised clustering
Neurocomputing
A neuromorphic approach to computer vision
Communications of the ACM
Fast rotation invariant multi-view face detection based on real adaboost
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Biologically Inspired Features for Scene Classification in Video Surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Enhanced Biologically Inspired Model for Object Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A computational model of visual cortex has raised great interest in developing algorithms mimicking human visual systems. The max-operation is employed in the model to emulate the scale and position invariant responses of the visual cells. We further extend this idea to enhance the tolerance of visual classification against the general intra-class variability. A general architecture of the basic block constituting the model is first presented. The architecture adaptively chooses the best matching template from a set of competing templates to predict the label of the incoming sample. To optimize the non-convex and non-smooth objective function resulted, we develop an algorithm to train each template alternately. Experiments show that the proposed method significantly outperforms linear classifiers as a template matching method in several image classification tasks, and is much more computationally efficient than other commonly used non-linear classifiers. In the image classification task on the Caltech 101 database, the performance of the biologically inspired model is obviously boosted by incorporating the proposed method.