Learning Parameter Tuning for Object Extraction

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.

[1]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  R Kahavi,et al.  Wrapper for feature subset selection , 1997 .

[3]  William K. Pratt,et al.  Digital image processing (2nd ed.) , 1991 .

[4]  James A. Sethian,et al.  A unified approach to noise removal, image enhancement, and shape recovery , 1996, IEEE Trans. Image Process..

[5]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[6]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[7]  J. Chris McGlone,et al.  Fusion of HYDICE hyperspectral data with panchromatic imagery for cartographic feature extraction , 1999, IEEE Trans. Geosci. Remote. Sens..

[8]  E. Baltsavias,et al.  Road network detection by mathematical morphology , 1999 .

[9]  Arcot Sowmya,et al.  INDUCTIVE CLUSTERING: AUTOMATING LOW-LEVEL SEGMENTATION IN HIGH RESOLUTION IMAGES * , 2002 .

[10]  Christian Heipke,et al.  EMPIRICAL EVALUATION OF AUTOMATICALLY EXTRACTED ROAD AXES , 1998 .

[11]  Anthony Stefanidis,et al.  Spatiospectral cluster analysis of elongated regions in aerial imagery , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[12]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[13]  Donald Geman,et al.  An Active Testing Model for Tracking Roads in Satellite Images , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[15]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[16]  Bir Bhanu,et al.  Adaptive image segmentation using a genetic algorithm , 1989, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Laurent D. Cohen,et al.  Global Minimum for Active Contour Models: A Minimal Path Approach , 1997, International Journal of Computer Vision.

[18]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[19]  Alexandru Telea,et al.  A Robust Level-Set Algorithm for Centerline Extraction , 2003, VisSym.

[20]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[21]  A. Baumgartner.  EFFICIENT METHODS AND INTERFACES FOR ROAD TRACKING , 2002 .

[22]  Xiongcai Cai,et al.  Learning to Recognise Roads from High Resolution Remotely Sensed Images , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[23]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[24]  Ivan Laptev,et al.  Automatic extraction of roads from aerial images based on scale space and snakes , 2000 .

[25]  Trish Keaton,et al.  Evolving roads in IKONOS multispectral imagery , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[26]  Aviad Zlotnick,et al.  Finding Road Seeds in Aerial Images , 1993 .

[27]  C. Steger,et al.  AUTOMATIC ROAD EXTRACTION BASED ON MULTI-SCALE, GROUPING, AND CONTEXT , 1999 .