Retrieving information from movies is becoming increasingly
demanding due to the enormous amount of multimedia
data generated each day. Not only it helps in efficient
search, archiving and classification of movies, but is also instrumental
in content censorship and recommendation systems.
Extracting key information from a movie and summarizing
it in a few tags which best describe the movie presents
a dedicated challenge and requires an intelligent approach
to automatically analyze the movie. In this paper, we formulate
movies tags extraction problem as a machine learning
classification problem and train a Convolution Neural Network
(CNN) on a carefully constructed tag vocabulary. Our
proposed technique first extracts key frames from a movie
and applies the trained classifier on the key frames. The
predictions from the classifier are assigned scores and are
filtered based on their relative strengths to generate a compact
set of most relevant key tags. We performed a rigorous
subjective evaluation of our proposed technique for a
wide variety of movies with different experiments. The evaluation
results presented in this paper demonstrate that our
proposed approach can efficiently extract the key tags of a
movie with a good accuracy
