With the rapid advancement of artificial intelligence, questions are increasingly being raised by stakeholders regarding how such technologies can enhance the environmental, social, and governance outcomes of organizations. In this study, challenges related to the organization and retrieval of video content within large, heterogeneous media archives are addressed. Existing methods, often reliant on human intervention or low-complexity algorithms, are observed to struggle with the growing demands of online video quantity and quality. To address these limitations, a novel approach is proposed, where convolutional neural networks and long short-term memory networks are utilized to extract both frame-level and temporal video features. Residual networks 50 (ResNet50) is integrated for enhanced content representation, and two-frame video flow is employed to improve system performance. The framework achieves precision, recall, and F-score of 79.2%, 86.5%, and 83%, respectively, on the YouTube, EPFL, and TVSum datasets. Beyond technological advancements, opportunities for effective content management are highlighted, emphasizing the promotion of sustainable digital practices. By minimizing data duplication and optimizing resource usage, scalable solutions for large media collections are supported by the proposed system.
In recent years, the rapid proliferation of online videos has led to an exponential increase in the volume of video content available on the internet. This explosion of data has introduced significant challenges in the organization, retrieval, and summarization of large video archives, particularly in the context of query-driven content searches. Platforms such as YouTube, Vimeo, and TikTok now host vast amounts of video data, making it increasingly difficult for users to efficiently locate relevant content. Addressing these issues has led to the emergence of AI as a promising tool for enhancing content management, improving video search algorithms, and streamlining video summarization processes.
Traditional video summarization techniques have relied heavily on manual annotation or simplistic algorithmic approaches, which often fall short in scalability and performance when handling large datasets. These methods typically fail to capture the temporal dynamics and frame-level features of videos, resulting in inaccurate or incomplete summaries. Hence, there is a pressing need for more sophisticated techniques that can automate video summarization and content retrieval with higher precision, recall, and overall effectiveness. Deep learning architectures, such as CNNs and LSTMnetworks, have shown potential to significantly improve the efficiency and accuracy of video summarization and content retrieval systems. By extracting frame-level and temporal features from videos, these technologies can create more relevant summaries and enhance content search results. This research proposes a novel approach that integrates CNNs, LSTMs, and the ResNet50 model with TVFlow to provide a scalable and high-performing framework for query-driven video summarization.
Many current video summarization and retrieval approaches often rely on basic algorithms or require substantial human intervention, limiting their ability to handle large, heterogeneous video datasets effectively. These methods often struggle with balancing both spatial and temporal features, essential for accurate video content retrieval. In contrast, our framework leverages the power of CNNs for spatial feature extraction, LSTM networks for temporal modeling, and the enhanced representation capabilities of ResNet50. The integration of TVFlow further improves system performance by capturing motion information, resulting in better retrieval accuracy and scalability for large video archives.
The exponential growth in video content has introduced significant challenges in effectively processing, managing, and summarizing large-scale video datasets. Traditional video summarization methods, often reliant on manual intervention or low-complexity algorithms, fall short when addressing the increasing volume and diversity of modern video data. As noted by Kadam et al. (2022), the intricate nature of electronically transmitted video content necessitates the development of advanced methodologies to meet evolving demands. Among these advancements, query-based video summarization has gained attention for its ability to generate user-specific summaries, offering points of interest tailored to individual queries. Recent works, such as those by Meena et al., emphasize the potential of semantically enhanced summarization techniques to deliver less redundant and more meaningful results. Additionally, Huang et al. have proposed pseudo-label supervision to further refine query-based summarization approaches. However, existing methods often struggle with scalability, adaptability, and capturing the diverse features of contemporary videos, highlighting a significant gap in current research.
To address these limitations, this study introduces a novel, scalable query-oriented video summarization framework. The proposed approach integrates advanced deep learning techniques, including CNNs, LSTMnetworks, and the ResNet50 model, for effective spatial and temporal feature extraction. Furthermore, TVFlow is employed to enhance temporal sequence analysis, ensuring more accurate and contextually relevant video summaries. The framework demonstrates robust performance, achieving precision (79.2%), recall (86.5%), and F-score (83%) on datasets such as YouTube, EPFL, and TVSum. By overcoming the limitations of traditional methodologies, this work paves the way for efficient, scalable, and user-driven video summarization, offering practical solutions for managing large and diverse media collections.
With the growing importance of video data mining, query-based video summarization has become a key area of focus. This approach aims to extract and present information from large video databases in a manner that aligns with the user's query criteria. The process generally involves several stages: segmenting the video to identify important segments, extracting relevant features, ranking the identified segments, and synthesizing a summarized version that meets the user's query requirements. This method not only accelerates the viewing process but also ensures that the content presented is more relevant to the user's preferences. Given the massive influx of video content, traditional video-watching methods are deemed inefficient for processing such large volumes of information, necessitating the use of automated systems for filtering and selection. Challenges such as the semantic gap between low-level video features and high-level content, user preference variations, and the dynamic nature of video data make achieving effective summarization complex. Enhanced machine learning (ML) approaches, including deep reinforcement learning, are being employed to improve the accuracy and flexibility of video summarization. Additionally, incorporating text and audio cues into summarization techniques has made summaries more personalized and relevant to user interests.
The increase in video information on the World Wide Web has resulted in an overwhelming amount of data, making it challenging for users to find relevant content efficiently. Query-dependent video summarization addresses this issue by providing compact and customized summaries based on user queries or preferences. Unlike traditional methods, which are slow and labor-intensive, this technique utilizes ML to process large volumes of video data, offering a more specific and time-efficient approach to presenting videos aligned with user interests. The benefits of query-based video summarization extend across various domains, enhancing social ROI. For instance, in online learning environments, students and educators benefit from compact summaries that contain crucial information, making learning sessions more targeted. Media organizations can leverage these techniques to quickly browse through extensive video libraries and select relevant content for news presentations. Marketers and content providers can create more engaging teasers and trailers, thereby increasing content effectiveness and viewer engagement.
This study presents a novel approach to query-driven video summarization by integrating advanced ML techniques with user-specific queries to generate highly personalized video summaries. The proposed method uniquely combines CNNs, LSTM networks, the ResNet50 model, and TVFlow, addressing the challenges posed by the exponential growth of video content on platforms like YouTube. By leveraging these cutting-edge technologies, substantial improvements in content retrieval efficiency and summary relevance are demonstrated, validated through high precision, recall, and F-score metrics on the YouTube dataset. This innovative framework enhances the scalability and performance of video summarization, offering practical solutions for managing and accessing vast video archives. Unlike traditional methods that rely on simpler algorithms or human intervention, this approach addresses the limitations of handling large-scale, complex video datasets, capturing both spatial and temporal features effectively. It provides a scalable solution for real-world applications such as automated video management and query-driven content retrieval in large media archives, making a significant contribution to the field of multimedia data access.
