Multimodal KDK classifier for automatic classification of movie trailers

4Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Movie trailer classification is a field of automation of analyzing the movie trailers and classify them into one of the various genres. In this paper, we proposed a classifier to identify the genre of a movie trailer by analyzing it's audio and visual features simultaneously. Our Approach decomposes a trailer video into frames and audio file and then analyze them based on certain specific features to categorize them into four genres. Our aim was to minimize the number of parameters involved in analyzing the trailer since other papers use many arguments which are impractical. The proposed classifier was trained on 4 audio, 2 broad visual features extracted from over 900 movie trailers distributed across 4 different genres, namely Drama, Horror, Romance, and Action. The Classifier Model has been trained using Neural Networks and Convolutional Neural Networks. Our Classifier Model can be used in Recommendation Systems and various websites like IMDB for automation of the genre classification process. As the common humanly approach is to generalize the results obtained from many inputs, the same way we use multiple models to obtain different outputs from multiple ANN models and then combine all the obtained results to get a final output. Also a Dataset containing 1000 movie trailers was introduced in this paper with trailers spanning to almost all Hollywood movies from 2010-2019. After training and conducting Experiments on around 1000 movie trailers, the classifier model showed a maximum accuracy of 81 percent in determining top 1 genre and 91 percent in determining top 2 genres of a movie trailers in the test set.

Cite

CITATION STYLE

APA

Shambharkar, P. G., Doja, M. N., Chandel, D., Bansal, K., & Taneja, K. (2019). Multimodal KDK classifier for automatic classification of movie trailers. International Journal of Recent Technology and Engineering, 8(3), 8481–8490. https://doi.org/10.35940/ijrte.C4825.098319

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free