Learning parameter tuning for object extraction

Authors:
Xiongcai Cai;Arcot Sowmya;John Trinder
Affiliations:
School of Computer Science and Engineering, University of New South Wales, and National ICT Australia, Sydney, Australia;School of Computer Science and Engineering, University of New South Wales, and National ICT Australia, Sydney, Australia;School of Surveying and Spatial Information Systems, University of New South Wales, Sydney, Australia
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Year:
2006

Citing 13
Cited 2

On active contour models and balloons

CVGIP: Image Understanding
Digital image processing (2nd ed.)

Digital image processing (2nd ed.)
Finding road seeds in aerial images

CVGIP: Image Understanding
Shape Modeling with Front Propagation: A Level Set Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Active Testing Model for Tracking Roads in Satellite Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Automatic extraction of roads from aerial images based on scale space and snakes

Machine Vision and Applications
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
A robust level-set algorithm for centerline extraction

VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Global Minimum for Active Contour Models: A Minimal Path Approach

CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Geodesic active contours

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A unified approach to noise removal, image enhancement, and shape recovery

IEEE Transactions on Image Processing

Level Learning Set: A Novel Classifier Based on Active Contour Models

ECML '07 Proceedings of the 18th European conference on Machine Learning
Active contour with neural networks-based information fusion kernel

ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a learning-based method for parameter tuning of object recognition systems and its application to automatic road extraction from high resolution remotely sensed (HRRS) images. Our approach is based on region growing using fast marching level set method (FMLSM), and machine learning for automatically tuning its parameters. FMLSM is used to extract the shape of objects in images. Parameters are introduced into the speed function of the FMLSM to improve flexibility and reflect the variety of images. The parameters are tuned using machine learning and utilizing background knowledge. The primary contribution of our approach is the ability to learn the parameters for a FMLSM model for object extraction. Experimental results on 11 HRRS image datasets, 1024*1024 pixels each with ground resolution of 1.3 meters, demonstrate the validity of the proposed algorithm. We are able to extract the roads without the use of heuristic parameters and other manual intervention.