Scale-invariant feature matching based on pairs of feature points

On the basis of feature points pairing, a scale-invariant feature matching method is proposed in this study. The distance between two features is used to compute feature pairs' support region size, which is different from the methods using detectors to provide information to find the support region. Moreover, to achieve rotation invariance, a sub-region division method based on intensity order is introduced. For comparison to the popular descriptors scale-invariant feature transform and speeded-up robust features, the authors also choose the detected points by difference of Gaussian and fast Hessain detectors as feature points to start the authors' method. Additional experiments compare the reported method with similar proposed methods, such as Tell's and Fan's. The experimental results show that the authors' proposed descriptor outperforms the popular descriptors under various image transformations, especially on images with scale and viewpoint transformations.

[1]  Tony Lindeberg,et al.  Shape-Adapted Smoothing in Estimation of 3-D Depth Cues from Affine Distortions of Local 2-D Brightness Structure , 1994, ECCV.

[2]  Bin Fan,et al.  Local Intensity Order Pattern for feature description , 2011, 2011 International Conference on Computer Vision.

[3]  Z. H. Wang,et al.  Feature vector field and feature matching , 2010, Pattern Recognit..

[4]  Cordelia Schmid,et al.  A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Stefan Carlsson,et al.  Wide Baseline Point Matching Using Affine Invariants Computed from Intensity Profiles , 2000, ECCV.

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

[7]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[8]  Cordelia Schmid,et al.  Maximally Stable Local Description for Scale Selection , 2006, ECCV.

[9]  Zhanyi Hu,et al.  Aggregating gradient distributions into intensity orders: A novel local image descriptor , 2011, CVPR 2011.

[10]  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..

[11]  Zhanyi Hu,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Rotationally Invariant Descript , 2011 .

[12]  Tony Lindeberg,et al.  Scale-Space for Discrete Signals , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Zhanyi Hu,et al.  Towards reliable matching of images containing repetitive patterns , 2011, Pattern Recognit. Lett..

[14]  Yannis Avrithis,et al.  Towards large-scale geometry indexing by feature selection , 2014, Comput. Vis. Image Underst..

[15]  Demetri Terzopoulos,et al.  Signal matching through scale space , 1986, International Journal of Computer Vision.

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

[17]  Dorin Comaniciu,et al.  Scale selection for anisotropic scale-space: application to volumetric tumor characterization , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

[19]  Yulei Wang,et al.  Visual tracking and learning using speeded up robust features , 2012, Pattern Recognit. Lett..

[20]  Hamid Soltanian-Zadeh,et al.  Radon transform orientation estimation for rotation invariant texture analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Antonis A. Argyros,et al.  Scale invariant and deformation tolerant partial shape matching , 2011, Image Vis. Comput..

[22]  Cordelia Schmid,et al.  Image matching with scale adjustment , 2004, Comput. Vis. Image Underst..

[23]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Salvatore Tabbone,et al.  Invariant pattern recognition using the RFM descriptor , 2012, Pattern Recognit..

[25]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.