Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strings: Variational Deformable Models of Multivariate Continuous Boundary Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Amsterdam Library of Object Images
International Journal of Computer Vision
A Six-Stimulus Theory for Stochastic Texture
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust photometric invariant features from the color tensor
IEEE Transactions on Image Processing
Hi-index | 0.00 |
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.