Recognizing image "style" and activities in video using local features and naive Bayes

The goal of this paper is to offer a framework for classification of images and video according to their "type", or "style"--a problem which is hard to define, but easy to illustrate; for example, identifying an artist by the Style of his/ her painting, or determining the activity in a video sequence. The paper offers a simple classification paradigm based on local properties of spatial or spatio-temporal blocks. The learning and classification are based on the naive Bayes classifier. A few experimental results are presented.

[1]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Lihi Zelnik-Manor,et al.  Event-based analysis of video , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Paul A. Viola,et al.  Texture recognition using a non-parametric multi-scale statistical model , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[4]  Edward H. Adelson,et al.  Noise removal via Bayesian wavelet coring , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[5]  R. Voss.  Local connected fractal dimension analysis of early Chinese landscape paintings and X-ray mammograms , 1998 .

[6]  Michel Vidal-Naquet,et al.  A Fragment-Based Approach to Object Representation and Classification , 2001, IWVF.

[7]  Michael Werman,et al.  Probabilistic Analysis of Regularization , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Rama Chellappa,et al.  Learning Texture Discrimination Rules in a Multiresolution System , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[10]  Ramprasad Polana,et al.  Temporal texture and activity recognition , 1994 .

[11]  Susan T. Dumais,et al.  Inductive learning algorithms and representations for text categorization , 1998, CIKM '98.

[12]  Paul A. Viola,et al.  Boosting Image Retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  David A. Forsyth,et al.  Learning the semantics of words and pictures , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[14]  David J. Fleet,et al.  Learning parameterized models of image motion , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  David H. Foster,et al.  Role of second- and third-order statistics in the discriminability of natural images , 1997 .

[16]  Hayit Greenspan,et al.  Color- and Texture-based Image Segmentation Using the Expectation-Maximization Algorithm and its Application to Content-Based Image Retrieval. , 1998, ICCV 1998.

[17]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[18]  W. Freeman,et al.  Learning local evidence for shading and reflectance , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[19]  David J. Fleet,et al.  Design and Use of Linear Models for Image Motion Analysis , 2000, International Journal of Computer Vision.

[20]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[23]  Randal C. Nelson,et al.  Detecting activities , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..