Comparison of affine-invariant local detectors and descriptors

In this paper we summarize recent progress on local photometric invariants. The photometric invariants can be used to find correspondences in the presence of significant viewpoint changes. We evaluate the performance of region detectors and descriptors. We compare several methods for detecting affine regions [4, 9, 11, 18, 17]. We evaluate the repeatability of the detected regions, the accuracy of the detectors and the invariance to geometric as well as photometric image transformations. Furthermore, we compare several local descriptors [3, 5, 8, 14, 19]. The local descriptors are evaluated in terms of two properties: robustness and distinctiveness. The evaluation is carried out for different image transformations and scene types. We observe that the ranking of the detectors and descriptors remains the same regardless the scene type or image transformation.

[2]  Andrew Zisserman,et al.  Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?" , 2002, ECCV.

[3]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[5]  Cordelia Schmid,et al.  3D object modeling and recognition using affine-invariant patches and multi-view spatial constraints , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  C. Schmid,et al.  Indexing based on scale invariant interest points , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  J. Koenderink,et al.  Representation of local geometry in the visual system , 1987, Biological Cybernetics.

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

[9]  Luc Van Gool,et al.  Affine/ Photometric Invariants for Planar Intensity Patterns , 1996, ECCV.

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

[11]  Tony Lindeberg,et al.  Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure , 1997, Image Vis. Comput..

[12]  Adam Baumberg,et al.  Reliable feature matching across widely separated views , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

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

[15]  Luc Van Gool,et al.  Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions , 2000, BMVC.

[16]  Luc Van Gool,et al.  Content-Based Image Retrieval Based on Local Affinely Invariant Regions , 1999, VISUAL.

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

[18]  Gustavo Carneiro,et al.  Phase-Based Local Features , 2002, ECCV.