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

arXiv: https://arxiv.org/abs/2503.21848 · HTML: https://mlsanity.com/paper/2503.21848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [MovieCLIP: Visual Scene Recognition in Movies](https://mlsanity.com/paper/2210.11065/) (2022)
- [Multiscale Audio Spectrogram Transformer for Efficient Audio Classification](https://mlsanity.com/paper/2303.10757/) (2023)
- [Learning Temporal Resolution in Spectrogram for Audio Classification](https://mlsanity.com/paper/2210.01719/) (2022)
- [Efficient Multiscale Multimodal Bottleneck Transformer for Audio-Video Classification](https://mlsanity.com/paper/2401.04023/) (2024)
- [We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline](https://mlsanity.com/paper/2402.00868/) (2024)
- [AI as a Tool for Fair Journalism: Case Studies from Malta](https://mlsanity.com/paper/2407.15316/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Hybrid Deterministic Framework for Named Entity Extraction in Broadcast News Video](https://mlsanity.com/paper/2602.09154/) (2026)

## Abstract (arXiv)

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.