Video content classification based on 3-D eigen analysis
To achieve video understanding, it is of utmost practical importance to classify videos according to its spatial and temporal features in an efficient and effective manner. It still remains, however, largely an elusive task. In still-image analysis, thanks to the great efforts made by many researchers, a broad spectrum of methods have been developed with great success, especially the ones based on eigen analysis due to its efficacy. In this paper, inspired by the impressive performance achieved by this framework, we will develop a content-based video classification method based on three-dimensional (3-D) eigen analysis. Unlike most other video understanding schemes where the spatial and temporal contents play different roles in the processing, this new method treats a video as a solid within a 3-D Euclidean space and can, thus, naturally take advantage of the spatial and temporal contents existing in videos. After computing the eigen values and corresponding eigen vectors of the autocorrelation matrix for each small 3-D macroblock, different labels are assigned regarding its spatial/temporal natures based on the behavioral properties of the eigen values and eigen vectors. Extensive empirical studies have suggested encouraging performance for the use of this eigen analysis-based video classification method.
eigen analysis video understanding content-based classification differential geometry higher space embedding