INTRODUCTION: In last few years number of internet users and available bandwidth has been increased exponentially. The availability of internet with such a low cost is making audiovisual content a more popular and easier form of information exchange. The internet is having a huge amount of this audiovisual content and to classify and choose a particular type of video is becoming a difficult task. A number of video classification methods (like text, audio and video feature extraction) have been proposed by researcher's community OBJECTIVES: This work is carried out to give a review of different video classification techniques and give a comparative analysis of available video classification techniques and to suggest the most accurate and efficient method of video classification. METHODS: Text, Audio and Visual video classification techniques. RESULTS: It has been observed that a combination of audio and visual feature extraction can provide better results. CONCLUSION: There are various methods of video classification either by using text, audio or video extraction. The text feature extraction is the least used method of video classification. The audio and visual feature extraction is being used in various applications but as we can understand that both the audio and visual feature extractions are having equal importance in video feature extraction but if we use combination of both these approaches, the results in form of accuracy of video classification can be further improved.

INTRODUCTION: As an essential part of life, the use of the Internet has increased exponentially. This rising Internet bandwidth speed has made video data transmission a more popular and modern form of information exchange. For classification of video date files there is a requirement of human efforts.Also for reducing the rate of clutter in video data on Internet, a suitable automatic video classification method is required. OBJECTIVES: In this work, we tried to find a successful model for video classification. METHODS: To make a successful model we use different schemes of visual and audio data analysis. On the other hand we choose some music, traffic and sports videos for different analysis. The model is based on Hidden Markov model (HMM) and Artificial neural network (ANN) classifiers.In order to gather the final results, we developed an "enhanced ANN-HMM based" model. RESULTS: Our approach attained an average of 90% success rate among all three classification classes. CONCLUSION: In aim of this work is to categorize and caption the videos automatically.Here we proposed an enhanced HMM-ANN based classification of video recordings with the aid of audio visual feature extraction.