Increasing public awareness and interest on access to visual information forces the creation of new technologies for representing

indexing

and retrieving multimedia data. For large image data and video libraries

use of efficient algorithms is necessary to enable fast browsing and access. Video abstract technology plays an important role in multimedia data processing and computer vision. Based on the clustering of the global or local features of an image

the video frames are clustered and the representative key frames are obtained. However

most of the existing methods need to determine the number of clusters in advance

and the adaptive method is inefficient in obtaining the clustering center. This paper presents a method for video key frame selection based on mapping and clustering. The difference between the different images was used to map the image to the corresponding point in 2D space

and the relative position and field density of points were used to cluster the points. Based on the results of the classification

a representative frame set was selected and used to constitute a video summary. Olivetti face database and Open Video database were used to test the proposed algorithm. Video summary results showed precision of 66% and recall of 74%. The value was 11%. Experimental results showed that the proposed method could effectively identify the image categories

which can then be used to quickly obtain the key frames in the video.