Image classification using cluster cooccurrence matrices of local relational features

Image classification systems have received a recent boost from methods using local features generated over interest points, delivering higher robustness against partial occlusion and cluttered backgrounds. We propose in this paper to use relational features calculated over multiple directions and scales around these interest points. Furthermore, a very important design issue is the choice of similarity measure to compare the bags of local feature vectors generated by each image, for which we propose a novel approach by computing image similarity using cluster co-occurrence matrices of local features. Excellent results are achieved for a widely used medical image classification task, and ideas to generalize to other tasks are discussed

[1]  Francesca Odone,et al.  Histogram intersection kernel for image classification , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[2]  Hermann Ney,et al.  Sparse Patch-Histograms for Object Classification in Cluttered Images , 2006, DAGM-Symposium.

[3]  Nicu Sebe,et al.  Efficient Object-Class Recognition by Boosting Contextual Information , 2005, IbPRIA.

[4]  Pietro Perona,et al.  Unsupervised Learning of Models for Recognition , 2000, ECCV.

[5]  James Ze Wang,et al.  Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Nicu Sebe,et al.  Wavelet-based salient points for image retrieval , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[7]  Vapnik,et al.  SVMs for Histogram Based Image Classification , 1999 .

[8]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  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.

[10]  Matti Pietikäinen,et al.  Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2000, ECCV.

[11]  Raphaël Marée,et al.  Biomedical Image Classification with Random Subwindows and Decision Trees , 2005, CVBIA.

[12]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[14]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[15]  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..

[16]  Marc Schael,et al.  Methoden zur Konstruktion invarianter Merkmale für die Texturanalyse , 2005 .

[17]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Michael Kohnen,et al.  The IRMA code for unique classification of medical images , 2003, SPIE Medical Imaging.

[19]  Julia Vogel,et al.  Semantic scene modeling and retrieval , 2004 .

[20]  Larry S. Davis,et al.  Texture Analysis Using Generalized Co-Occurrence Matrices , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[22]  Hermann Ney,et al.  Discriminative training for object recognition using image patches , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  Hermann Ney,et al.  Classification of Medical Images using Non-linear Distortion Models , 2004, Bildverarbeitung für die Medizin.

[24]  Nicu Sebe,et al.  Comparing salient point detectors , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[25]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[26]  Michael Kohnen,et al.  Quality of DICOM header information for image categorization , 2002, SPIE Medical Imaging.