Robust Tracking Using Foreground-Background Texture Discrimination
International Journal of Computer Vision
Robust Scene Categorization by Learning Image Statistics in Context
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quasi-periodic spatiotemporal filtering
IEEE Transactions on Image Processing
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We review the state of the art in content-based exploration of large sets of image and video data. The key to any current approach is features to which still many new additions have been made in the last five years). We discuss image features on a global, regional, and key point level employing stochastic textures derived from natural image statistics and Gaussian color invariant feature sets [Geusebroek 2006]. Features evolving from analysis moving images are still under the largest evolution. In this topic we will discuss several advances towards robust and long-term tracking [Nguyen 2005, Nguyen 2006, Burghouts 2006. Then we move on to review the state of the art in retrieval systems as it has evolved since "the end of the early years" in 2000. We do so on the basis of the components and evaluation of our video search engine. The MediaMill system performed well in the TRECvid competition in the years 2004 to 2006 [Snoek 2006] and provide a perspective the future of search engines.