C4.5: programs for machine learning
C4.5: programs for machine learning
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Saliency, Scale and Image Description
International Journal of Computer Vision
Visual Recognition Using Local Appearance
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Context based object detection from video
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Q-learning of sequential attention for visual object recognition from informative local descriptors
ICML '05 Proceedings of the 22nd international conference on Machine learning
PhoneGuide: museum guidance supported by on-device object recognition on mobile phones
MUM '05 Proceedings of the 4th international conference on Mobile and ubiquitous multimedia
Reinforcement Learning for Decision Making in Sequential Visual Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Object recognition and detection represent a relevant component in cognitive computer vision systems, such as in robot vision, intelligent video surveillance systems, or multimodal interfaces. Object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clutter in detection tasks. This work contributes to this research by a thorough analysis of the discriminative power of local appearance patterns and by proposing to exploit local information content to model object representation and recognition. We identify discriminative regions in the object views from a posterior entropy measure, and then derive object models from selected discriminative local patterns. For recognition, we determine rapid attentive search for locations of high information content from learned decision trees. The recognition system is evaluated by various degrees of partial occlusion and Gaussian image noise, resulting in highly robust recognition even in the presence of severe occlusion effects.