Video contains the most affluent information but implies huge storage and complicated semantics. To search for required fragments among huge quantity of video is a tedious and time-consuming task for traditional manual indexing and sequential searching methods which certainly cannot meet the performance requirements of video databases. What the users want is to query by contents

that is

to get the desired fragments of video with just some given examples or feature descriptions. Because of the complicated structure and temporal variation of video data

it is very difficult to index video by content. Researchers have worked out various methods and techniques to solve the problem. The essential steps for content-based video indexing are video segmentation

key frame selection

static and dynamic feature extraction and video clustering. Starting from a brief description of the structures and characteristics of video

this paper generalizes the methods and techniques used in content-based video indexing

analyses in depth the newly proposed ones and their respective advantages and drawbacks. As a conclusion

the paper discusses some of the problems in content-based video indexing that are worth to be tackled in future researches.