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Mood Detection in Aesthetically Appealing Video Based on Color Association

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Abstract

Aesthetic comes under the branch of cognitive science, and it plays a significant role in demonstrating the psychology of humans. Videos or images that are more colourful attracts lots of audiences. At the same time, these colour characteristic helps in finding the mood of a person whether they are happy, sad, anger, fear etc. Many approaches such as machine learning technique, neural network were designed earlier for achieving mood detection in appealing video. However, effective detection with enhanced accuracy was not attained in conventional methods. For overcoming these drawbacks, Deep Convolutional Neural Network (DCNN) based approach is designed in this proposed work to perform aesthetic classification and mood detection. At the beginning of the process video is converted into frames and key frame extraction is performed using histogram method. Then, foreground portion is separated from the background using object detection technique based on Gaussian Mixture Model (GMM). After that, extraction of low level, high level features and aesthetic classification is done using DCNN. Finally, using pleasing video the mood detection is done based on color features. The proposed method is implemented and its performances are evaluated using measures including accuracy, precision, recall, and F1 score whose values are 77.4, 77.4 77.9 and 77.4%. Based on this proposed approach automated and effective aesthetic classification can be achieved along with human mood detection.

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References

  1. Kuang, Q., Jin, X., Zhao, Q., & Zhou, B. (2019). Deep multimodality learning for UAV video aesthetic quality assessment. IEEE Transactions on Multimedia, 22(10), 2623–2634.

    Article  Google Scholar 

  2. Liu, X., & Jiang, Y. (2021). Aesthetic assessment of website design based on multimodal fusion. Future Generation Computer Systems, 117, 433–438.

    Article  Google Scholar 

  3. Ahmad, F., Hariharan, U., Karthick, S., Pawar, V. E., & Sharon Priya, S. (2023). Optimized lung nodule prediction model for lung cancer using contour features extraction. Optical Memory and Neural Networks, 32(2), 126–136.

    Article  Google Scholar 

  4. Fang-Lue, Z., Wu, X., Li, R.-L., Wang, J., Zheng, Z.-H., & Hu, S.-M. (2018). Detecting and removing visual distractors for video aesthetic enhancement. IEEE Transactions on Multimedia, 20(8), 1987–1999.

    Article  Google Scholar 

  5. Karthick, S., & Muthukumaran, N. (2023). Deep regression network for single-image super-resolution based on down-and upsampling with RCA blocks. National Academy Science Letters, 1–5. https://doi.org/10.1007/s40009-023-01353-5

  6. Yueying, K., He, R., & Huang, K. (2017). Deep aesthetic quality assessment with semantic information. IEEE Transactions on Image Processing, 26(3), 1482–1495.

    Article  MathSciNet  Google Scholar 

  7. Hou, J., Ding, H., Lin, W., Liu, W., & Fang, Y. (2022). Distilling knowledge from object classification to aesthetics assessment. IEEE Transactions on Circuits and Systems for Video Technology, 32(11), 7386–7402.

    Article  Google Scholar 

  8. Perumal Sankar, S., Vishwanath, N., Jer Lang, H., & Karthick, S. (2017). An Effective content based medical image retrieval by using ABC based artificial neural network (ANN). Current Medical Imaging Reviews, 13(3), 223–230.

    Google Scholar 

  9. Kim, W. H., Choi, J. H., & Lee, J. S. (2018). Objectivity and subjectivity in aesthetic quality assessment of digital photographs. IEEE Transactions on Affective Computing, 11(3), 493–506.

    Article  Google Scholar 

  10. Niu, Y., Chen, S., Song, B., Chen, Z., & Liu, W. (2022). Comment-guided semantics-aware image aesthetics assessment. IEEE Transactions on Circuits and Systems for Video Technology, 33(3), 1487–1492.

    Article  Google Scholar 

  11. Nelson, R., Mao, Q., Wang, L., Gou, J., & Dong, M. (2019). Mood-aware visual question answering. Neurocomputing, 330, 305–316.

    Article  Google Scholar 

  12. Kun-Yi, H., Wu, C.-H., & Su, M.-H. (2019). Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses. Pattern Recognition, 88, 668–678.

    Article  Google Scholar 

  13. Sukamto, R. A., & Handoko, S. (2017). Learners mood detection using Convolutional Neural Network (CNN). In 2017 3rd International Conference on Science in Information Technology (ICSITech) (pp. 18-22). IEEE.

  14. Giannakakis, G., Pediaditis, M., Manousos, D., Kazantzaki, E., Chiarugi, F., Simos, P. G., & Tsiknakis, M. (2017). Stress and anxiety detection using facial cues from videos. Biomedical Signal Processing and Control, 31, 89–101.

    Article  Google Scholar 

  15. Xin, Lu., Lin, Z., Jin, H., Yang, J., & Wang, J. Z. (2015). Rating image aesthetics using deep learning. IEEE Transactions on Multimedia, 17(11), 2021–2034.

    Article  Google Scholar 

  16. Hsin-Ho, Y., Yang, C.-Y., Lee, M.-S., & Chen, C.-S. (2013). Video aesthetic quality assessment by temporal integration of photo-and motion-based features. IEEE Transactions on Multimedia, 15(8), 1944–1957.

    Article  Google Scholar 

  17. Shasha, Mo., Niu, J., Su, Y., & Das, S. K. (2018). A novel feature set for video emotion recognition. Neurocomputing, 291, 11–20.

    Article  Google Scholar 

  18. Wang, W., Zhao, M., Wang, L., Huang, J., Cai, C., & Xu, X. (2016). A multi-scene deep learning model for image aesthetic evaluation. Signal Processing: Image Communication, 47, 511–518.

    Google Scholar 

  19. Kuanar, S. K., Panda, R., & Chowdhury, A. S. (2013). Video key frame extraction through dynamic Delaunay clustering with a structural constraint. Journal of Visual Communication and Image Representation, 24(7), 1212–1227.

    Article  Google Scholar 

  20. Hegadi, R. S., & Goudannavar, B. A. (2011). Interactive segmentation of medical images using grabcut. International Journal of Machine Intelligence, 3(3), 168–171.

    Article  Google Scholar 

  21. Ellis, L., & Ficek, C. (2001). Color preferences according to gender and sexual orientation. Personality and Individual Differences, 31(8), 1375–1379.

    Article  Google Scholar 

  22. Palmer, S. E., Schloss, K. B., & Sammartino, J. (2013). Visual aesthetics and human preference. Annual review of psychology, 64, 77–107.

    Article  Google Scholar 

  23. Schloss, K. B., & Palmer, S. E. (2009). An ecological valence theory of human color preferences. Journal of Vision, 9(8), 358–358.

    Article  Google Scholar 

  24. https://nofilmschool.com/color-psychology-in-film

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Authors and Affiliations

  1. Department of Computer Science and Engg, Dr Vishwanath Karad Maharashtra Institute of Technology WPU University: Maharashtra Institute of Technology WPU, Survey No, 124, Paud Rd, Kothrud, Pune, Maharashtra, 411038, India

    Madhura Phatak

  2. Tata Consultancy Services-Research, TCS Pune, Pune, India

    Manasi Patwardhan

  3. Department of Computer Science and Engg, G.H. Raisoni College of Engineering (GHRCOE), Digdoh Hills, Nagpur, Maharashtra, 440016, India

    Prashant Borkar

Authors
  1. Madhura Phatak
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  2. Manasi Patwardhan
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  3. Prashant Borkar
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Contributions

MP is contributed for methodology, design and analysis; MP is contributed for writing, reviewing and editing and PB is contributed for proofreading, reviewing and editing.

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Correspondence to Madhura Phatak.

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Phatak, M., Patwardhan, M. & Borkar, P. Mood Detection in Aesthetically Appealing Video Based on Color Association. Wireless Pers Commun 133, 1349–1372 (2023). https://doi.org/10.1007/s11277-023-10796-4

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