Embedded double matching of local descriptors for a fast automatic recognition of real-world objects

In this paper, we present a new approach for matching local descriptors such as Scale Invariant Feature Transform (SIFT) ones in order to recognize image objects quickly and reliably. The proposed method first computes the Hausdorff distance combined with the City-Block distance to match the two sets of the extracted keypoints from the goal and data images, respectively. Then, the matched points are involved into an embedded pairing process, leading to a double matching which is more discriminant for the object recognition purpose as demonstrated on real-world standard databases.

[1]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[2]  Marjorie Skubic,et al.  Performance Evaluation of SIFT-Based Descriptors for Object Recognition , 2010 .

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

[4]  Wei Liang,et al.  Packed Dense Interest Points for Scene Image Retrieval , 2011, 2011 Sixth International Conference on Image and Graphics.

[5]  Yong Deng,et al.  A new Hausdorff distance for image matching , 2005, Pattern Recognit. Lett..

[6]  Ming-Hsuan Yang,et al.  A New Affine Registration Algorithm for Matching 2D Point Sets , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).

[7]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[8]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[9]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Chao Zhu,et al.  Visual object recognition using DAISY descriptor , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[11]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Jean-Michel Jolion,et al.  Content based image retrieval using interest points and texture features , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[13]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Joanna I. Olszewska,et al.  Ontology-coupled active contours for dynamic video scene understanding , 2011, 2011 15th IEEE International Conference on Intelligent Engineering Systems.

[15]  Hao Wang,et al.  MOCC: A Fast and Robust Correlation-Based Method for Interest Point Matching under Large Scale Changes , 2010, EURASIP J. Adv. Signal Process..

[16]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[17]  Joanna Isabelle Olszewska,et al.  Lighting-variable AdaBoost Based-on System for Robust Face Detection , 2012, BIOSIGNALS.

[18]  K. Shadan,et al.  Available online: , 2012 .

[19]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[21]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .