Large Model based Sequential Keyframe Extraction for Video Summarization

Abstract: Keyframe extraction aims to sum up a video's semantics with the minimum number of its frames. This paper puts forward a Large Model based Sequential Keyframe Extraction for video summarization, dubbed LMSKE, which contains three stages as below. First, we use the large model "TransNetV21" to cut the video into consecutive shots, and employ the large model "CLIP2" to generate each frame's visual feature within each shot; Second, we develop an adaptive clustering algorithm to yield candidate keyframes for each shot, with each candidate keyframe locating nearest to a cluster center; Third, we further reduce the above candidate keyframes via redundancy elimination within each shot, and finally concatenate them in accordance with the sequence of shots as the final sequential keyframes. To evaluate LMSKE, we curate a benchmark dataset and conduct rich experiments, whose results exhibit that LMSKE performs much better than quite a few SOTA competitors with average F1 of 0.5311, average fidelity of 0.8141, and average compression ratio of 0.9922.

• The paper presents LMSKE, a sequential keyframe extraction method leveraging TransNetV2 and CLIP to segment videos and extract semantic features.
• It employs an adaptive clustering algorithm based on SSE and Silhouette Coefficient, followed by redundancy elimination to ensure compact and meaningful summaries.
• Experimental results on TVSum20 demonstrate superior F1 scores and temporal fidelity compared to state-of-the-art video summarization techniques.

Large Model-Based Sequential Keyframe Extraction for Video Summarization

Introduction

The paper "Large Model based Sequential Keyframe Extraction for Video Summarization" (2401.04962) presents an innovative approach to summarizing video content through keyframe extraction by leveraging large-scale models. This method is particularly relevant in today's context where platforms such as YouTube and TikTok have made video content a ubiquitous part of daily life. Keyframe extraction plays a crucial role in video storage, retrieval, and analysis by selecting a minimal yet informative set of frames to represent the video's visual semantics.

Methodology

The proposed method, referred to as LMSKE, is a three-stage process involving shot segmentation, adaptive clustering, and redundancy elimination. The approach demonstrates sophistication in its use of large models, particularly TransNetV2 for shot segmentation and CLIP for extracting detailed frame-level semantic features.

Shot Segmentation and Feature Extraction

The process begins with TransNetV2 cutting the video into consecutive shots, which allows for an organized structure to approach summarization. CLIP is then employed to extract semantic features from each frame within these shots. The use of CLIP's high-dimensional feature representations is critical in maintaining the depth of semantic content throughout the process, contrasting with traditional methods reliant on simpler features like SIFT or HOG.

Adaptive Clustering

A novel adaptive clustering algorithm is introduced to partition frame features within each shot. The approach automatically determines the optimal cluster count using the Sum of Squared Errors (SSE) and Silhouette Coefficient (SC) metrics. This stage clusters frames into segments, with each cluster's center representing a candidate keyframe. The refinement of clustering parameters to maximize SC ensures the selection of keyframes that best encapsulate each shot's content.

Redundancy Elimination

The redundancy elimination phase targets both non-informative and duplicate keyframes, contributing to a compact yet comprehensive summary. This is achieved by employing color histogram comparisons for solid-color frames and a similarity matrix for redundancy assessment. The method's iterative refinement ensures the final keyframe set preserves the video's semantic integrity while minimizing excess data.

Experimental Evaluation

The LMSKE method was validated on the curated TVSum20 dataset, a benchmark collection derived from the broader TVSum dataset, optimized for keyframe extraction evaluation. Experiments demonstrate the method's superior performance across F1, Fidelity, and Compression Ratio (CR) metrics. Specifically, LMSKE achieves an average F1 score of 0.5311, outperforming state-of-the-art competitors such as INCEPTION and VSUMM, particularly in maintaining temporal sequence fidelity while compressing video content effectively.

Implications and Future Directions

The implications of this research extend to fields involving video analytics, media archiving, and real-time video content summarization. By leveraging large models, the method ensures a scalable approach to video summarization that adapts to varying video contents and complexities. Future developments could explore integrating more advanced large models, potentially incorporating generative approaches to refine redundancy elimination or adaptive clustering processes further.

Conclusion

The "Large Model based Sequential Keyframe Extraction for Video Summarization" presents a robust method that efficiently condenses video content through intelligent keyframe extraction. By combining the power of large models with an innovative clustering algorithm, LMSKE marks a significant advancement in video summarization technology, delivering both theoretical insights and practical tools for video content management. The provision of the TVSum20 dataset publicly fosters further advancements and comparative evaluations within the domain.