Unsupervised Multi-Modal Video Summarization (UMVS)

Introduction

The UMVS project aims to generate concise and meaningful video summaries by leveraging multiple data modalities (audio, visual, object detection, and textual metadata). By integrating advanced mathematical modeling and optimization techniques, UMVS eliminates the need for labeled datasets, making it scalable and adaptable for diverse video summarization tasks.

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Problem Statement

Given a video ( V ), represented as a sequence of ( n ) frames ( F = {, , ,} ), and additional modalities such as:

• Audio ( A ),
• Objects ( O ),
• Titles ( T ),

The goal is to select a subset that satisfies the following:

1. Maximizing Coverage:
   

2. Ensuring Diversity:
   

3. Minimizing Redundancy:
   

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Mathematical Framework

Multi-Modal Feature Representation

Each frame is encoded as a vector , integrating features from multiple modalities:

1. Visual Features ):
   Visual features are extracted using the VGG16 model:
   
   Dimensionality is reduced via Autoencoders:
   

2. Audio Features ):
   Audio features are extracted using Mel-Frequency Cepstral Coefficients (MFCC):
   

3. Object Features :
   Objects within frames are detected using the YOLOv5 model:
   

4. Title Features ):
   Titles are encoded into semantic vectors using BERT-based NLP models:
   

The final feature representation for each frame combines all these modalities:

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Frame and Clip Importance

1. Frame Importance:
   Frame importance is determined by the cosine similarity between object features and title features:
   

2. Clip Importance:
   Clip importance is calculated as the sum of the importance values of its constituent frames:
   

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Optimization for Summarization

The summarization process is modeled as a 0/1 Knapsack Problem:

1. Objective:
   Maximize the importance of selected frames:
   

2. Constraints:

   • Total duration must not exceed the allowed maximum:
     
   • Binary decision variable:
     

Here, ) indicates that frame is included in the summary.

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Results and Insights

Dataset

The UMVS framework was evaluated using the TVSum-50 dataset, which includes diverse videos with varying content.

Key Metrics

• Coverage: ( 87% )
• Diversity: ( 91% )
• F-Score: ( 0.89 )

Additional Observations

1. Dimensionality Reduction:
   The use of autoencoders reduced the dimensionality of visual features from 480,000 to 1024, improving computational efficiency.

2. Knapsack Optimization:
   Outperformed traditional clustering-based methods by selecting the most significant clips for summarization.

3. Semantic Alignment:
   The cosine similarity metric effectively aligned detected objects with titles, resulting in accurate frame importance scores.

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Conclusion

The UMVS framework demonstrates the effectiveness of mathematical modeling and multi-modal integration in video summarization. By utilizing unsupervised learning, it eliminates the need for labeled datasets while maintaining scalability and adaptability for various applications.

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References

1. Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556
2. Redmon, J., et al. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv:1506.02640
3. Davis, S. B., & Mermelstein, P. (1980). Comparison of Parametric Representations for Monosyllabic Word Recognition.

For more details, visit the GitHub Repository.