Using temporal texture for content-based video retrieval

Textures evolving over time are called temporal textures and are very commo n in everyday life. Examples are the smoke flowing or the wavy water of a r iver. The idea explored in this paper is that image features based on tempo ral texture could allow a better performance of current content-based video retrieval systems that are mainly based on static characteristics of repre sentative frames, like color and texture. To this aim we analyze the spatio -temporal nature of texture and its application in content-based access to video databases. In particular, we represent temporal texture using the spa tiotemporal autoregressive (STAR) model and a variation of self-organizing maps (SOM) where each node is an autoregressive model. These representation schemes have been implemented in a query by example framework to analyze t he weaknesses and the strengths of the different approaches. Preliminary ex perimental results are reported. (C) 2000 Academic Press.