Representation of local geometry in the visual system
Biological Cybernetics
Deformable Kernels for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Gaussian Derivative Filters
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
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
Empirical Study of Multi-scale Filter Banks for Object Categorization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Oriented filters for object recognition: an empirical study
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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The goal of this paper is to study the set of features that is suitable for describing animals in images, and for being able to categorize them in natural scenes. We propose multi-scale features based on Gaussian derivatives functions, that show interesting invariance properties. In order to build an efficient system, we will use classifiers based on the JointBoosting methodology, which will be compared with the well-known one-vs-all approach by using Support Vector Machines. Thirty five categories, containing animals, are selected from the challenging Caltech 101 object categories database to carry out the study.