ct 20(24): e2

Research Article

Automatic Video Classification: A Review

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  • @ARTICLE{10.4108/eai.13-7-2018.163996,
        author={Pooja Rani and Jaspreet Kaur and Sahil Kaswan},
        title={Automatic Video Classification: A Review},
        journal={EAI Endorsed Transactions on Creative Technologies},
        keywords={Video Classification, Audiovisual, Feature Extraction, Bandwidth, Analysis, Review},
  • Pooja Rani
    Jaspreet Kaur
    Sahil Kaswan
    Year: 2020
    Automatic Video Classification: A Review
    DOI: 10.4108/eai.13-7-2018.163996
Pooja Rani1,*, Jaspreet Kaur1, Sahil Kaswan1
  • 1: JCDM College of Engineering, Sirsa Haryana 125055, India
*Contact email: poojamehta0193@gmail.com


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.