Elements of information theory
Elements of information theory
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Feature Hierarchies for Object Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Vase or Face? A Neural Correlate of Shape-Selective Grouping Processes in the Human Brain
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Bio-inspired Connectionist Architecture for Visual Detection and Refinement of Shapes
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Bio-inspired architecture for human detection
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Journal of Cognitive Neuroscience
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Object-related areas in the ventral visual system in humans are known from imaging studies to be preferentially activated by object images compared with noise or texture patterns. It is unknown, however, which features of the object images are extracted and represented in these areas. Here we tested the extent to which the representation of visual classes used object fragments selected by maximizing the information delivered about the class. We tested functional magnetic resonance imaging blood oxygenation level-dependent activation of highly informative object features in low-and high-level visual areas, compared with noninformative object features matched for low-level image properties. Activation in V1 was similar, but in the lateral occipital area and in the posterior fusiform gyrus, activation by “informative” fragments was significantly higher for three object classes. Behavioral studies also revealed high correlation between performance and fragments information. The results show that an objective class-information measure can predict classification performance and activation in human object-related areas.