Semantic Kernels Binarized - A Feature Descriptor for Fast and Robust Matching

This paper presents a new approach for feature description used in image processing and robust image recognition algorithms such as 3D camera tracking, view reconstruction or 3D scene analysis. State of the art feature detectors distinguish interest point detection and description. The former is commonly performed in scale space, while the latter is used to describe a normalized support region using histograms of gradients or similar derivatives of the grayscale image patch. This approach has proven to be very successful. However, the descriptors are usually of high dimensionality in order to achieve a high descriptiveness. Against this background, we propose a binarized descriptor which has a low memory usage and good matching performance. The descriptor is composed of binarized responses resulting from a set of folding operations applied to the normalized support region. We demonstrate the real-time capabilities of the feature descriptor in a stereo matching environment.

[1]  Matthew A. Brown,et al.  Invariant Features from Interest Point Groups , 2002, BMVC.

[2]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

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

[4]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[5]  Luc Van Gool,et al.  Efficient Non-Maximum Suppression , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[6]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

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

[8]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  Otthein Herzog,et al.  Binarising SIFT-Descriptors to Reduce the Curse of Dimensionality in Histogram-Based Object Recognition , 2009, FGIT-SIP.

[10]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[12]  Mosalam Ebrahimi,et al.  SUSurE: Speeded Up Surround Extrema feature detector and descriptor for realtime applications , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[13]  Andrew Zisserman,et al.  An Affine Invariant Salient Region Detector , 2004, ECCV.

[14]  Jonathan Brandt,et al.  Transform coding for fast approximate nearest neighbor search in high dimensions , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[16]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[17]  Tobias Höllerer,et al.  Fast and scalable keypoint recognition and image retrieval using binary codes , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[18]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[19]  Bernd Girod,et al.  Compressed Histogram of Gradients: A Low-Bitrate Descriptor , 2011, International Journal of Computer Vision.

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

[21]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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