Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture (2024)

research-article

Authors: Fangru Lin, Jie Yuan, Zhiwei Chen, and Maryam Abiri

Journal of Cloud Computing, Volume 13, Issue 1

Published: 17 May 2024 Publication History

  • 0citation
  • 0
  • Downloads

Metrics

Total Citations0Total Downloads0

Last 12 Months0

Last 6 weeks0

  • Get Citation Alerts

    New Citation Alert added!

    This alert has been successfully added and will be sent to:

    You will be notified whenever a record that you have chosen has been cited.

    To manage your alert preferences, click on the button below.

    Manage my Alerts

    New Citation Alert!

    Please log in to your account

      • View Options
      • References
      • Media
      • Tables
      • Share

    Abstract

    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.

    References

    [1]

    Chen Z, Ye S, Chu X, Xia H, Zhang H, Qu H, and Wu Y Augmenting sports videos with viscommentator IEEE Trans Visual Comput Graph 2021 28 1 824-34

    [2]

    Ma J, Jiang X, Fan A, Jiang J, and Yan J Image matching from handcrafted to deep features: a survey Int J Comput Vision 2021 129 23-79

    Digital Library

    [3]

    Wang W, Yang Y, Wang X, Wang W, and Li J Development of convolutional neural network and its application in image classification: a survey Opt Eng 2019 58 4 040901

    [4]

    Saini P, Kumar K, Kashid S, Saini A, and Negi A Video summarization using deep learning techniques: a detailed analysis and investigation Artif Intell Rev 2023 56 11 12347-12385

    Digital Library

    [5]

    Singh AS, Bevilacqua A, Nguyen TL, Hu F, McGuinness K, O’Reilly M, and Ifrim G Fast and robust video-based exercise classification via body pose tracking and scalable multivariate time series classifiers Data Min Knowl Discov 2023 37 2 873-912

    Digital Library

    [6]

    Yang Y, Qi Y, and Qi S Relation-consistency graph convolutional network for image super-resolution Vis Comput 2024 40 2 619-635

    Digital Library

    [7]

    Kumar S, Kumar N, Dev A, and Naorem S Movie genre classification using binary relevance, label powerset, and machine learning classifiers Multimed Tools Appl 2023 82 1 945-968

    Digital Library

    [8]

    Dastbaravardeh, E., et al., (2024). Channel Attention-Based Approach with Autoencoder Network for Human Action Recognition in Low-Resolution Frames. Int J Intell Syst. 2024

    [9]

    Motamedi E, Kholgh DK, Saghari S, Elahi M, Barile F, and Tkalcic M Predicting movies’ eudaimonic and hedonic scores: a machine learning approach using metadata, audio and visual features Inf Process Manag 2024 61 2 103610

    Digital Library

    [10]

    Yousaf K and Nawaz T A deep learning-based approach for inappropriate content detection and classification of youtube videos IEEE Access 2022 28 10 16283-98

    [11]

    Yi Y, Li A, and Zhou X Human action recognition based on action relevance weighted encoding Signal Process 2020 1 80 115640

    [12]

    Almeida A, de Villiers JP, De Freitas A, and Velayudan M The complementarity of a diverse range of deep learning features extracted from video content for video recommendation Expert Syst Appl 2022 15 192 116335

    Digital Library

    [13]

    Mahadevkar SV, Khemani B, Patil S, Kotecha K, Vora DR, Abraham A, and Gabralla LA A review on machine learning styles in computer vision—Techniques and future directions IEEE Access 2022 26 10 107293-329

    [14]

    Tulbure AA, Tulbure AA, and Dulf EH A review on modern defect detection models using DCNNs–Deep convolutional neural networks J Adv Res 2022 1 35 33-48

    [15]

    Montalvo-Lezama R, Montalvo-Lezama B, and Fuentes-Pineda G Improving transfer learning for movie trailer genre classification using a dual image and video transformer Inf Process Manag 2023 60 3 103343

    Digital Library

    [16]

    Bi T, Jarnikov D, Lukkien J. (2022 ) Shot-Based Hybrid Fusion for Movie Genre Classification. InInternational Conference on Image Analysis and Processing. pp. 257-269. Cham: Springer International Publishing

    [17]

    Pant P, Sai Sabitha A, Choudhury T, and Dhingra P Multi-label classification trending challenges and approaches Emerg Trends Expert Appl Secur 2018 2019 433-44

    [18]

    Wehrmann J and Barros RC Movie genre classification: a multi-label approach based on convolutions through time Appl Soft Comput 2017 1 61 973-82

    [19]

    Zhang X and Yang Q Transfer hierarchical attention network for generative dialog system Int J Autom Comput 2019 16 720-36

    Digital Library

    [20]

    Rezaee K et al. A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance Personal and Ubiquitous Computing 2024 28 1 135-151

    Digital Library

    [21]

    Badamdorj T, Rochan M, Wang Y, Cheng L. (2021) Joint visual and audio learning for video highlight detection. InProceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8127-8137

    [22]

    Tian Y, Xu C. (2021) Can audio-visual integration strengthen robustness under multimodal attacks?. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5601-5611

    [23]

    Zhou H, Hermans T, Karandikar AV, Rehg JM. (2010) Movie genre classification via scene categorization. InProceedings of the 18th ACM international conference on Multimedia. pp. 747-750

    [24]

    Cai Z, Ding H, Wu J, Xi Y, Wu X, and Cui X Multi-label movie genre classification based on multimodal fusion Multimed Tools Appl 2023 15 1-8

    [25]

    Yang X, Esquivel JA. (2023) LSTM network-based Adaptation Approach for Dynamic Integration in Intelligent End-edge-cloud Systems. Tsinghua Sci Technol

    [26]

    Li D and Esquivel JA Accuracy-enhanced E-commerce recommendation based on deep learning and locality-sensitive hashing Wireless Networks 2024 2 1-6

    [27]

    Li D, Esquivel JA. Trust-aware Hybrid Collaborative Recommendation with Locality-Sensitive Hashing. Tsinghua Science and Technology. 2023.

    [28]

    Rasheed Z, Sheikh Y, and Shah M On the use of computable features for film classification IEEE Trans Circuits Syst Video Technol 2005 15 1 52-64

    [29]

    Jain SK, Jadon RS. (2009 ) Movies genres classifier using neural network. In2009 24th International Symposium on Computer and Information Sciences. pp. 575-580.

    [30]

    Huang YF, Wang SH. (2012) Movie genre classification using svm with audio and video features. InActive Media Technology: 8th International Conference, AMT 2012, Macau, China, December 4-7, 2012. Proceedings 8 pp. 1-10. Springer Berlin Heidelberg

    [31]

    Oliva A and Torralba A Modeling the shape of the scene: a holistic representation of the spatial envelope Int J Comput Vision 2001 42 145-75

    Digital Library

    [32]

    Wu J, Rehg JM. (2008) Where am I: Place instance and category recognition using spatial PACT. In2008 IEEE Conference on Computer Vision and Pattern Recognition pp. 1-8

    [33]

    Simoes GS, Wehrmann J, Barros RC, Ruiz DD. (2016) Movie genre classification with convolutional neural networks. In2016 International Joint Conference on Neural Networks (IJCNN) pp. 259-266

    [34]

    Ogawa T, Sasaka Y, Maeda K, and Haseyama M Favorite video classification based on multimodal bidirectional LSTM IEEE Access 2018 18 6 61401-9

    [35]

    Ben-Ahmed O, Huet B. (2018) Deep multimodal features for movie genre and interestingness prediction. In2018 international conference on content-based multimedia indexing (CBMI) pp. 1-6. IEEE

    [36]

    Aytar Y, Vondrick C, Torralba A. (2016) Soundnet: Learning sound representations from unlabeled video. Adv Neural Inf Process Syst ;29

    [37]

    Álvarez F, Sánchez F, Hernández-Peñaloza G, Jiménez D, Menéndez JM, and Cisneros G On the influence of low-level visual features in film classification PloS One 2019 14 2 e0211406

    [38]

    Yu Y, Lu Z, Li Y, and Liu D ASTS: attention based spatio-temporal sequential framework for movie trailer genre classification Multimed Tools Appl 2021 80 9749-64

    [39]

    Varghese J, Ramachandran Nair KN. (2019) A novel video genre classification algorithm by keyframe relevance. InInformation and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018, Volume 1 pp. 685-696. Springer Singapore

    [40]

    Choroś K Fast method of video genre categorization for temporally aggregated broadcast videos J Intell Fuzzy Syst 2019 37 6 7657-67

    [41]

    Yadav A and Vishwakarma DK A unified framework of deep networks for genre classification using movie trailer Appl Soft Comput 2020 1 96 106624

    Digital Library

    [42]

    Jiang Y and Zheng L Deep learning for video game genre classification Multimed Tools Appl 2023 17 1-5

    [43]

    Mangolin RB, Pereira RM, Britto AS Jr, Silla CN Jr, Feltrim VD, Bertolini D, and Costa YM A multimodal approach for multi-label movie genre classification Multimed Tools Appl 2022 81 14 19071-96

    Digital Library

    [44]

    Behrouzi T, Toosi R, and Akhaee MA Multimodal movie genre classification using recurrent neural network Multimed Tools Appl 2023 82 4 5763-84

    Digital Library

    [45]

    Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. (2009) Imagenet: A large-scale hierarchical image database. In2009 IEEE conference on computer vision and pattern recognition pp. 248-255

    [46]

    Tan M, Le Q. (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. InInternational conference on machine learning. pp. 6105-6114. PMLR

    [47]

    Wehrmann J, Barros RC. (2017) Convolutions through time for multi-label movie genre classification. InProceedings of the Symposium on Applied Computing. pp. 114-119

    [48]

    Yang X and Esquivel JA Time-aware LSTM neural networks for dynamic personalized recommendation on business intelligence Tsinghua Sci Technol 2023 29 1 185-96

    [49]

    Mu Y and Wu Y Multimodal movie recommendation system using deep learning Mathematics 2023 11 4 895

    [50]

    Zhang Z, Gu Y, Plummer BA, Miao X, Liu J, Wang H. (2024) Movie genre classification by language augmentation and shot sampling. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 7275-7285

    [51]

    Tabatabaei S et al. Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system Biomed Signal Process Control 2023 1 86 105119

    [52]

    Ullah W, Hussain T, Ullah FU, Lee MY, and Baik SW TransCNN: Hybrid CNN and transformer mechanism for surveillance anomaly detection Eng Appl Artif Intell 2023 1 123 106173

    Digital Library

    Recommendations

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

      Abstract

      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 ...

      Read More

    • A unified framework of deep networks for genre classification using movie trailer

      Abstract

      Affective video content analysis has emerged as one of the most challenging and essential research tasks as it aims to analyze the emotions elicited by videos automatically. However, little progress has been achieved in this field due ...

      Highlights

      • Development of a unified framework of deep networks for movie genre classification.

      Read More

    • A hybrid deep learning approach for classification of music genres using wavelet and spectrogram analysis

      Abstract

      Manual classification of millions of songs of the same or different genres is a challenging task for human beings. Therefore, there should be a machine intelligent model that can classify the genres of the songs very accurately. In this paper, a ...

      Read More

    Comments

    Information & Contributors

    Information

    Published In

    Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture (1)

    Journal of Cloud Computing: Advances, Systems and Applications Volume 13, Issue 1

    Jun 2024

    1821 pages

    ISSN:2192-113X

    EISSN:2192-113X

    Issue’s Table of Contents

    © The Author(s) 2024.

    Publisher

    Hindawi Limited

    London, United Kingdom

    Publication History

    Published: 17 May 2024

    Accepted: 07 May 2024

    Received: 19 March 2024

    Author Tags

    1. Video classification
    2. Deep learning
    3. Service management
    4. Cloud computing
    5. Movie genres
    6. Bidirectional LSTM
    7. EfficientNet

    Qualifiers

    • Research-article

    Contributors

    Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture (2)

    Other Metrics

    View Article Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Total Citations

    • Total Downloads

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0

    Other Metrics

    View Author Metrics

    Citations

    View Options

    View options

    Get Access

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    Get this Publication

    Media

    Figures

    Other

    Tables

    Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture (2024)

    References

    Top Articles
    Latest Posts
    Article information

    Author: Roderick King

    Last Updated:

    Views: 5885

    Rating: 4 / 5 (51 voted)

    Reviews: 90% of readers found this page helpful

    Author information

    Name: Roderick King

    Birthday: 1997-10-09

    Address: 3782 Madge Knoll, East Dudley, MA 63913

    Phone: +2521695290067

    Job: Customer Sales Coordinator

    Hobby: Gunsmithing, Embroidery, Parkour, Kitesurfing, Rock climbing, Sand art, Beekeeping

    Introduction: My name is Roderick King, I am a cute, splendid, excited, perfect, gentle, funny, vivacious person who loves writing and wants to share my knowledge and understanding with you.