Rotation Invariant Spatial Pyramid Matching for Image Classification

This paper proposes a new Spatial Pyramid representation approach for image classification. Unlike the conventional Spatial Pyramid, the proposed method is invariant to rotation changes in the images. This method works by partitioning an image into concentric rectangles and organizing them into a pyramid. Each pyramidal region is then represented using a histogram of visual words. Our experimental results show that our proposed method significantly outperforms the conventional method.

[1]  Guojun Lu,et al.  Improved Spatial Pyramid Matching for Image Classification , 2010, ACCV.

[2]  Cordelia Schmid,et al.  A maximum entropy framework for part-based texture and object recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[3]  Guojun Lu,et al.  Image indexing and retrieval based on vector quantization , 2007, Pattern Recognit..

[4]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[5]  Naftali Tishby,et al.  Agglomerative Information Bottleneck , 1999, NIPS.

[6]  Fahad Shahbaz Khan,et al.  Discriminative compact pyramids for object and scene recognition , 2012, Pattern Recognition.

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Shawn D. Newsam,et al.  Spatial pyramid co-occurrence for image classification , 2011, 2011 International Conference on Computer Vision.

[9]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

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

[12]  Jitendra Malik,et al.  Shape matching and object recognition using low distortion correspondences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  Yasuo Kuniyoshi,et al.  Discriminative spatial pyramid , 2011, CVPR 2011.

[15]  Theo Gevers,et al.  Geometry-constrained spatial pyramid adaptation for image classification , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[16]  Xuelong Li,et al.  Beyond Spatial Pyramids: A New Feature Extraction Framework with Dense Spatial Sampling for Image Classification , 2012, ECCV.

[17]  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..