Part-Based Statistical Models for Object Classification and Detection

Authors:
Elliot Joel Bernstein;Yali Amit
Affiliations:
University of Chicago;University of Chicago
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Year:
2005

Citing 0
Cited 7

Sharing Visual Features for Multiclass and Multiview Object Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
POP: Patchwork of Parts Models for Object Recognition

International Journal of Computer Vision
Object categorization

Foundations and Trends® in Computer Graphics and Vision
Learning to Locate Informative Features for Visual Identification

International Journal of Computer Vision
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection

International Journal of Computer Vision
Hierarchical part-based detection of 3d flexible tubes: application to CT colonoscopy

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
A boundary-fragment-model for object detection

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose using simple mixture models to define a set of mid-level binary local features based on binary oriented edge input. The features capture natural local structures in the data and yield very high classification rates when used with a variety of classifiers trained on small training sets, exhibiting robustness to degradation with clutter. Of particular interest are the use of the features as variables in simple statistical models for the objects thus enabling likelihood based classification. Pre-training decision boundaries between classes, a necessary component of non-parametric techniques, is thus avoided. Class models are trained separately with no need to access data of other classes. Experimental results are presented for handwritten character recognition, classification of deformed LATEX symbols involving hundreds of classes, and side view car detection.