The complementarity of a diverse range of deep learning features extracted from video content for video recommendation

Almeida, Adolfo; Villiers, J. P. de; Freitas, Allan De; Velayudan, Mergandran

doi:10.1016/j.eswa.2021.116335

Cited by 11 publications

(2 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visual media, such as images and videos, have become increasingly popular for sharing information due to their simplicity of creation and distribution [1][2][3]. The complexity of video perception has increased with the incorporation of temporal elements into spatial video, challenging imagebased approaches.…”

Section: Introductionmentioning

confidence: 99%

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

Shao,

Guo

2024

J Cloud Comp

View full text Add to dashboard Cite

It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

show abstract

Section: Introductionmentioning

confidence: 99%

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

Shao,

Guo

2024

J Cloud Comp

View full text Add to dashboard Cite

show abstract

“…Genres can be classified into specific instances [9,10], and duties can be executed [11]. Several computer vision applications, such as video retrieval and recommendation systems, rely on video categorization [12,13]. The implementation of convolutional neural networks (CNNs) in computer vision has led to significant progress [14].…”

Section: Introductionmentioning

confidence: 99%

Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture

Lin,

Yuan,

Chen

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

Film and movie genres play a pivotal role in captivating relevant audiences across interactive multimedia platforms. With a focus on entertainment, streaming providers are increasingly prioritizing the automatic generation of movie genres within cloud-based media services. In service management, the integration of a hybrid convolutional network proves to be instrumental in effectively distinguishing between a diverse array of video genres. This classification process not only facilitates more refined recommendations and content filtering but also enables targeted advertising. Furthermore, given the frequent amalgamation of components from various genres in cinema, there arises a need for social media networks to incorporate real-time video classification mechanisms for accurate genre identification. In this study, we propose a novel architecture leveraging deep learning techniques for the detection and classification of genres in video films. Our approach entails the utilization of a bidirectional long- and short-term memory (BiLSTM) network, augmented with video descriptors extracted from EfficientNet-B7, an ImageNet pre-trained convolutional neural network (CNN) model. By employing BiLSTM, the network acquires robust video representations and proficiently categorizes movies into multiple genres. Evaluation on the LMTD dataset demonstrates the substantial improvement in the performance of the movie genre classifier system achieved by our proposed architecture. Notably, our approach achieves both computational efficiency and precision, outperforming even the most sophisticated models. Experimental results reveal that EfficientNet-BiLSTM achieves a precision rate of 93.5%. Furthermore, our proposed architecture attains state-of-the-art performance, as evidenced by its F1 score of 0.9012.

show abstract

Micro video recommendation in multimodality using dual-perception and gated recurrent graph neural network

Patil,

Kotwal

2023

Multimed Tools Appl

View full text Add to dashboard Cite

The complementarity of a diverse range of deep learning features extracted from video content for video recommendation

Cited by 11 publications

References 42 publications

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture

Micro video recommendation in multimodality using dual-perception and gated recurrent graph neural network

Contact Info

Product

Resources

About