Comparative Analysis of Image, Video, and Audio Classifiers for Automated News Video Segmentation

2025-03-27

AI Summary

News videos exhibit loose structural coherence, posing significant challenges for automated segmentation and content recognition. This paper systematically evaluates the classification performance of image-based (ResNet), video-based (ViViT), audio-based (AST), and multimodal models across five segment types: advertisements, news reports, studio scenes, transitions, and visualizations. Experiments are conducted on a newly curated dataset comprising 41 news videos and 1,832 manually annotated segments. Results demonstrate that lightweight image models substantially outperform complex temporal models: ResNet achieves 84.34% accuracy in five-way classification, while binary classification of transitions and advertisements reaches 94.23% and 92.74% accuracy, respectively. These findings challenge the prevailing assumption that effective video understanding necessitates explicit temporal modeling, and instead establish that static frame features are both highly efficient and robust for news video segmentation. The work provides a computationally lightweight, frame-based paradigm for automated news video processing—particularly advantageous in resource-constrained environments.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

News videos require efficient content organisation and retrieval systems, but their unstructured nature poses significant challenges for automated processing. This paper presents a comprehensive comparative analysis of image, video, and audio classifiers for automated news video segmentation. This work presents the development and evaluation of multiple deep learning approaches, including ResNet, ViViT, AST, and multimodal architectures, to classify five distinct segment types: advertisements, stories, studio scenes, transitions, and visualisations. Using a custom-annotated dataset of 41 news videos comprising 1,832 scene clips, our experiments demonstrate that image-based classifiers achieve superior performance (84.34% accuracy) compared to more complex temporal models. Notably, the ResNet architecture outperformed state-of-the-art video classifiers while requiring significantly fewer computational resources. Binary classification models achieved high accuracy for transitions (94.23%) and advertisements (92.74%). These findings advance the understanding of effective architectures for news video segmentation and provide practical insights for implementing automated content organisation systems in media applications. These include media archiving, personalised content delivery, and intelligent video search.

Problem

• Comparing image, video, audio classifiers for news segmentation
• Evaluating deep learning models for classifying news segment types
• Optimizing automated content organization in news video systems

Innovation

• Comparative analysis of image, video, audio classifiers
• ResNet outperforms complex temporal models efficiently
• Binary models excel in transition and ad classification