GenVis: Visualizing Genre Detection in Movie Trailers for Enhanced Understanding

Abstract

Automatic movie genre detection is vital for improving content recommendations, user experiences, and organization. Multi-label generation detection assigns multiple labels to a movie and recognizes a movie’s diverse themes. Although there are many existing methods for generating multiple genre labels from movies but do not provide comprehensive analysis and visual depiction. This work introduces GenVis, a visualization system that provides a better understanding of multi-label genres extracted from movie trailers. The system initially uses text and visual features to classify trailers and assign multiple genre labels and probabilities. Next, GenVis provides four visualization views: a video view for trailer observation, an overall genre view for getting insights into genre distribution, a genre timeline view for temporal genre evolution, and finally, a genre flow summary for more focused genre analysis. The system allows users to pause the frames, sort the results, and process multiple videos. The multi-label classification is rigorously evaluated using MSE, cross-entropy loss, precision, recall, F1-score metrics, achieving high accuracy, and demonstrating strong genre correlations with notable precision in effectively classifying and distinguishing movie genres. Additionally, a user evaluation for visualization evaluation demonstrated the effectiveness and intuitive usability of GenVis with a high overall rating of 4.25 out of 5.0.

Keywords

• Genre detection
• movie trailers
• video visualization
• data visualization
• video analysis