Création de Vocabulaires Visuels Efficaces pour la Catégorisation d'Images

Nous proposons dans cet article une methode de construction automatique de vocabulaires visuels. Le vocabulaire visuel est obtenu par quantification de descripteurs locaux des images. Les vocabulaires visuels produits sont utilises pour construire automatiquement des representations discriminantes des objets presents dans les images. Nous decrivons une application de ces techniques a la categorisation d'images par sacs de primitives (bags of features) et montrons que les resultats obtenus sont tres superieurs a ceux obtenus par les methodes concurrentes.

[1]  Editors , 1986, Brain Research Bulletin.

[2]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[3]  D. Geman,et al.  Invariant Statistics and Coding of Natural Microimages , 1998 .

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

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

[6]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[7]  Marko Grobelnik,et al.  Interaction of Feature Selection Methods and Linear Classification Models , 2002 .

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

[9]  C. Greg Plaxton,et al.  The Online Median Problem , 1999, SIAM J. Comput..

[10]  B. Schiele,et al.  Interleaved Object Categorization and Segmentation , 2003, BMVC.

[11]  Tomaso A. Poggio,et al.  Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..

[12]  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..

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

[14]  Shimon Ullman,et al.  Object recognition with informative features and linear classification , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  Ilan Shimshoni,et al.  Mean shift based clustering in high dimensions: a texture classification example , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

[17]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[18]  C. Schmid,et al.  Scale-invariant shape features for recognition of object categories , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[19]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[20]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[21]  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.

[22]  Hans C. van Houwelingen,et al.  The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .

[23]  Gary M. Weiss.  Mining with rarity: a unifying framework , 2004, SKDD.

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

[25]  Frédéric Jurie,et al.  Creating efficient codebooks for visual recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[26]  Christopher K. I. Williams,et al.  Pascal Visual Object Classes Challenge Results , 2005 .