A Textural Based Hidden Markov Model for Animation Genre Discrimination
Joseph Santarcangelo, Xiao-Ping Steven Zhang · 2012
This paper develops a novel method to automatically categorize different animation genres in a video database made for children, this is the first such research done in animation genre categorization. The method is based on statistically modeling the temporal texture attributes of the video sequence. The features are extracted from gray-level co-occurrence matrices and a hidden Markov models (HMM) are used as a classifier. It was found the method had 16.66% better accuracy compared to other methods with the same number of parameters and dimensions of feature vector.