Video Segmentation Utilized in Visual Data Mining Applications

A novel approach utilized in video database objectbased queries is proposed. This new method segments an example video sequence into real world objects using a combination of color image segmentation techniques along with MPEG-1/2 motion vectors. First, the initial frame in the sequence undergoes a color/texture segmentation algorithm that divides the frame into homogenous regions of color and texture. Features extracted from the co-occurrence matrix provide the texture measurement utilized by this system. The MPEG-1/2 motion vectors are then exploited in predicting the objects of the next frame based on the initial frame’s segmentation. A robust technique for the identification of new objects as they enter the scene is proposed along with a measurement pertaining to the object segmentation accuracy is introduced. This new method has a host of visual data mining applications including image database retrieval and Internet image searches. This new algorithm is tested on various image sequences with segmentation results included.