Large Model based Sequential Keyframe Extraction for Video Summarization (2401.04962v1)

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 introduces LMSKE, a three-stage approach that harnesses large models for precise shot segmentation and semantic feature extraction.
• It employs adaptive clustering to select representative, diverse keyframes and eliminates redundancy using color histogram and similarity checks.
• Experimental results on TVSum20 demonstrate improved F1 scores and compression ratios, enhancing video indexing and retrieval efficiency.

Large Model based Sequential Keyframe Extraction for Video Summarization (LMSKE)

Introduction

In recent developments within the domain of video processing, keyframe extraction has emerged as a critical approach for summarizing the visual content of videos. This technique aims to distill the semantic essence of a video into a minimal set of frames, facilitating tasks such as video storage, retrieval, and analysis. The paper introduces a novel approach, the Large Model based Sequential Keyframe Extraction (LMSKE), which leverages the capabilities of large models for efficient shot segmentation and the extraction of semantically rich visual features. Through an innovative three-stage process that includes shot segmentation, adaptive clustering, and redundancy elimination, LMSKE provides a robust solution for generating sequential keyframes that effectively summarize video content.

Methodology

Shot Segmentation and Feature Extraction

The initial stage involves employing the large model TransNetV2 for precise video shot segmentation, while CLIP is used for extracting semantic features of each frame within shots. This ensures that the extracted keyframes are representative of the video's varied content.

Adaptive Clustering

Following the segmentation, an adaptive clustering algorithm is applied to each shot, determining the optimal number of clusters and subsequently generating candidate keyframes situated closest to cluster centers. This approach guarantees that the selected keyframes are both representative and diverse, effectively summarizing the shot's content.

Redundancy Elimination

The final stage focuses on removing redundant or insufficiently informative frames from the candidate keyframe set. Through a combination of color histogram analysis and similarity checking, the method efficiently identifies and excludes frames that offer minimal additional semantic information, ensuring the final keyframe set is both compact and comprehensive.

Experimental Results

The evaluation of LMSKE on a curated benchmark dataset (TVSum20) reveals its superior performance over state-of-the-art methods, with notable improvements in F1 score, fidelity, and compression ratio metrics. This evidences LMSKE's ability to generate more semantically rich and condensed video summaries compared to its contemporaries.

Practical Implications

LMSKE's advanced approach to keyframe extraction has significant implications for numerous applications in video content management and analysis. Its ability to create concise, semantically rich video summaries can enhance the efficiency of video indexing, search, and retrieval systems, making it easier to manage and explore large video databases. Furthermore, the proposed method's reliance on large models for feature extraction and its adaptive clustering algorithm set new benchmarks in the field, potentially inspiring future research and development in video summarization technologies.

Future Directions

The paper sets a solid foundation for further exploration into the integration of large models within video summarization tasks. As the field advances, there are opportunities to refine these models for even more nuanced understanding and representation of video content. Moreover, extending the approach to incorporate audio and text analysis could yield more comprehensive multimedia summarization tools, capable of capturing the full spectrum of content within videos.

Conclusion

LMSKE stands out as an effective approach to video keyframe extraction, significantly improving upon existing methodologies in terms of semantic richness and summarization efficiency. Its innovative use of large models for shot segmentation and feature extraction, combined with an adaptive clustering technique for keyframe selection, establishes a new standard in the field. As video continues to dominate digital communication channels, the importance of efficient summarization tools like LMSKE will only grow, making its contributions both timely and impactful.