Enhancing image registration performance by incorporating distribution and spatial distance of local descriptors

Abstract A data dependency similarity measure called mp-dissimilarity has been recently proposed. Unlike lp-norm distance which is widely used in calculating the similarity between vectors, mp-dissimilarity takes into account the relative positions of the two vectors with respect to the rest of the data. This paper investigates the potential of mp-dissimilarity in matching local image descriptors. Moreover, three new matching strategies are proposed by considering both lp-norm distance and mp-dissimilarity. Our proposed matching strategies are extensively evaluated against lp-norm distance and mp-dissimilarity on a few benchmark datasets. Experimental results show that mp-dissimilarity is a promising alternative to lp-norm distance in matching local descriptors. The proposed matching strategies outperform both lp-norm distance and mp-dissimilarity in matching accuracy. One of our proposed matching strategies is comparable to lp-norm distance in terms of recall vs 1-precision.

[1]  Xiong Fengguang,et al.  A 3D Surface Matching Method Using Keypoint- Based Covariance Matrix Descriptors , 2017, IEEE Access.

[2]  Guojun Lu,et al.  Improved Symmetric-SIFT for Multi-modal Image Registration , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.

[3]  C. Krumhansl.  Concerning the applicability of geometric models to similarity data: The interrelationship between similarity and spatial density. , 1978 .

[4]  Zhi-Hua Zhou,et al.  Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure , 2016, KDD.

[5]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Gholamreza Haffari,et al.  Mp-Dissimilarity: A Data Dependent Dissimilarity Measure , 2014, 2014 IEEE International Conference on Data Mining.

[7]  Amin Sedaghat,et al.  Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Ali Ismail Awad,et al.  Image Feature Detectors and Descriptors , 2016 .

[9]  Tal Hassner,et al.  LATCH: Learned arrangements of three patch codes , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[10]  Adrien Bartoli,et al.  KAZE Features , 2012, ECCV.

[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]  Gholamreza Haffari,et al.  Data-dependent dissimilarity measure: an effective alternative to geometric distance measures , 2017, Knowledge and Information Systems.

[13]  Shiliang Zhang,et al.  Edge-SIFT: Discriminative Binary Descriptor for Scalable Partial-Duplicate Mobile Search , 2013, IEEE Transactions on Image Processing.

[14]  Charles V. Stewart,et al.  Keypoint Descriptors for Matching Across Multiple Image Modalities and Non-linear Intensity Variations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Chia-Ling Tsai,et al.  Registration of Challenging Image Pairs: Initialization, Estimation, and Decision , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Peng Zhang,et al.  New Point Matching Algorithm Using Sparse Representation of Image Patch Feature for SAR Image Registration , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Jie Tian,et al.  A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration , 2010, IEEE Transactions on Biomedical Engineering.

[19]  Amin Sedaghat,et al.  Remote Sensing Image Matching Based on Adaptive Binning SIFT Descriptor , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[20]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

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

[22]  Guojun Lu,et al.  Multimodal image registration technique based on improved local feature descriptors , 2015, J. Electronic Imaging.

[23]  Guojun Lu,et al.  Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors , 2016, Pattern Recognit. Lett..

[24]  Stefan Winkler,et al.  A general framework for image feature matching without geometric constraints , 2016, Pattern Recognit. Lett..

[25]  Elena Deza,et al.  Encyclopedia of Distances , 2014 .

[26]  Sabine Süsstrunk,et al.  Illuminant estimation and detection using near-infrared , 2009, Electronic Imaging.

[27]  Max A. Viergever,et al.  Efficient and reliable schemes for nonlinear diffusion filtering , 1998, IEEE Trans. Image Process..

[28]  Jong Beom Ra,et al.  Multi-sensor image registration based on intensity and edge orientation information , 2008, Pattern Recognit..

[29]  Jie Tian,et al.  Real-time multi-modal rigid registration based on a novel symmetric-SIFT descriptor , 2009 .

[30]  Gholamreza Haffari,et al.  Half-space mass: a maximally robust and efficient data depth method , 2015, Machine Learning.