The scene segmentation is a high-level temporal video segment.This paper presents a method of scene segmentation based on semantics.At first

the video clips are segmented into shots and the shot key frames are extracted.Then the features of color histogram and MPEG-7 edge histogram of each key frame are computed and the feature vectors of shot key frames are formed.The support vector machines(SVM) are trained by these feature vectors and 7 binary classifiers in accordance with difference semantic concepts are constructed.These binary classifiers are used to classify the shot key frames of the video clips based on the features of the color and the texture and the semantics concepts of shot key frames can be obtained.The semantic concept vectors of shot key frames are formed by the semantic concepts contained in the key frames.The shot key frames are clustered by the semantic concept vectors and the video scene can be constructed.The shot select function is defined to extract the scene key frame based on the value of function.The experimental results shown that the recall and the precision of this algorithm are higher than those of the Hanjalic's method about 34.7% and 9.1%

respectively.