Multimedia Analysis by Learning

In this presentation for the panel at MCAM07, I put forward the transition of modeling the world as was done on a large scale in computer vision before the year 2000, to the current situation where there have been considerable successes with multimedia analysis by learning from the world. We make a plead for the last type of learned features, modeling only the scene accidental conditions and learning the object or object class intrinsic properties. In this paper, in respect to contributions by many others, we illustrate the approach of learning features by papers from our lab at the University of Amsterdam.

[1]  Arnold W. M. Smeulders,et al.  Strings: Variational Deformable Models of Multivariate Continuous Boundary Features , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Marcel Worring,et al.  The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Arnold W. M. Smeulders,et al.  c ○ 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Six-Stimulus Theory for Stochastic Texture , 2002 .

[4]  Arnold W. M. Smeulders,et al.  Color Invariance , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Joost van de Weijer,et al.  Robust photometric invariant features from the color tensor , 2006, IEEE Transactions on Image Processing.

[6]  Arnold W. M. Smeulders,et al.  The Amsterdam Library of Object Images , 2004, International Journal of Computer Vision.

[7]  Gertjan J. Burghouts,et al.  Color invariant object recognition using entropic graphs , 2006, Int. J. Imaging Syst. Technol..

[8]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..