Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Textured Motion: Particle, Wave and Sketch
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we study mathematical models of atomic visual patterns from natural videos and establish a generative visual vocabulary for video representation. Empirically, we employ small video patches (e.g., 15×15×5, called video "bricks") in natural videos as basic analysis unit. There are a variety of brick subspaces (or atomic video words) of varying dimensions in the high dimensional brick space. The structures of the words are characterized by both appearance and motion dynamics. Here, we categorize the words into two pure types: structural video words (SVWs) and textural video words (TVWs). A common generative model is introduced to model these two type video words in a unified form. The representation power of a word is measured by its information gain, based on which words are pursued one by one via a novel pursuit algorithm, and finally a holistic video vocabulary is built up. Experimental results show the potential power of our framework for video representation.