Pattern recognition with local invariant features

Local invariant features have shown to be very successful for recognition. They are robust to occlusion and clutter, distinctive as well as invariant to image transformations. In this chapter recent progress on local invariant features is summarized. It is explained how to extract scale and affine-invariant regions and how to obtain discriminant descriptors for these regions. It is then demonstrated that combining local features with pattern classification techniques allows for texture and category-level object recognition in the presence of varying viewpoints and background clutter.

[1]  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..

[2]  Kenichi Kanatani,et al.  Geometric Information Criterion for Model Selection , 1998, International Journal of Computer Vision.

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

[4]  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).

[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]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

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

[8]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[10]  Cordelia Schmid,et al.  Selection of scale-invariant parts for object class recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  James L. Crowley,et al.  A Representation for Shape Based on Peaks and Ridges in the Difference of Low-Pass Transform , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Andrew Zisserman,et al.  Classifying Images of Materials: Achieving Viewpoint and Illumination Independence , 2002, ECCV.

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

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

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

[17]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

[19]  Cordelia Schmid,et al.  Affine-invariant local descriptors and neighborhood statistics for texture recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[21]  Dorin Comaniciu,et al.  Performance analysis in content-based retrieval with textures , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[22]  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..

[23]  Cordelia Schmid,et al.  Semi-Local Affine Parts for Object Recognition , 2004, BMVC.

[24]  Ronen Basri,et al.  The Alignment Of Objects With Smooth Surfaces , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[25]  David A. Forsyth,et al.  Canonical Frames for Planar Object Recognition , 1992, ECCV.

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

[27]  Dan Roth,et al.  Learning a Sparse Representation for Object Detection , 2002, ECCV.

[28]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[29]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Peter Auer,et al.  Weak Hypotheses and Boosting for Generic Object Detection and Recognition , 2004, ECCV.

[31]  Cordelia Schmid,et al.  Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval , 2004, International Journal of Computer Vision.

[32]  Max A. Viergever,et al.  General intensity transformations and differential invariants , 1994, Journal of Mathematical Imaging and Vision.

[33]  Andrew Zisserman,et al.  Viewpoint invariant texture matching and wide baseline stereo , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

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

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

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

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

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

[39]  Cordelia Schmid,et al.  Human Detection Based on a Probabilistic Assembly of Robust Part Detectors , 2004, ECCV.

[40]  Luis Álvarez,et al.  Affine Morphological Multiscale Analysis of Corners and Multiple Junctions , 1997, International Journal of Computer Vision.