Object Evidence Extraction Using Simple Gabor Features and Statistical Ranking

Several novel methods based on locally extracted object features and spatial constellation models have recently been introduced for invariant object detection and recognition. The accuracy and reliability of the methods depend on the success of both tasks: evidence extraction and spatial constellation model search. In this study an accurate and efficient method for evidence extraction is introduced. The proposed method is based on simple Gabor features and their statistical ranking.

[1]  Jiri Matas,et al.  XM2VTSDB: The Extended M2VTS Database , 1999 .

[2]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[3]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  Miroslav Hamouz.  Feature-based affine-invariant detection and localization of faces , 2004 .

[5]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Joni-Kristian Kämäräinen.  Feature extraction using Gabor filters , 2003 .

[7]  Timor Kadir,et al.  Scale Saliency and Scene Description , 2002 .

[8]  Josef Kittler,et al.  Affine-invariant face detection and localization using GMM-based feature detector and enhanced appearance model , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[9]  Joni-Kristian Kämäräinen,et al.  Simple Gabor feature space for invariant object recognition , 2004, Pattern Recognit. Lett..

[10]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[11]  Joni-Kristian Kämäräinen,et al.  Feature representation and discrimination based on Gaussian mixture model probability densities - Practices and algorithms , 2006, Pattern Recognit..

[12]  Martin A. Fischler,et al.  The Representation and Matching of Pictorial Structures , 1973, IEEE Transactions on Computers.