A Thorough Evaluation of Discrete Cosine Transform for Content Based Video Classification

With the growth in data collection through the use of media, it is necessary to classify this data in its resident format and with a good classification accuracy. Today the majority of the volume of data being gathered or shared is in the form of videos. Major multimedia applications are being developed to capture more and more high quality videos of different types. Its crucial to know what techniques are necessary to classify this video data in such a way that the data can be easily retrieved and presented to the user instead of making the user choose from too many parameters. This paper presents the experimental results carried out on two data sets, viz., Cartoon and Sports video datasets. The paper presents, compares and concludes the combination of Data mining techniques that should be used along with Discrete Cosine Transform in order to achieve high accuracy of video classification.