Open Access Open Access  Restricted Access Subscription Access

Machine Learning Model based Image and Video Colorization

Vikram Sindhu, Muhammed Shuraim Shuru, Afshan Sanera, Firdose Khanum M, Kashifuddin Saiyad

Abstract


In this paper, we present an intuitive framework for clients to effectively colorize the regular pictures of complex scenes. In our framework, colorization system is expressly isolated into two phases: Shading naming and Shading planning. Pixels that ought to generally have comparative tones are assembled into lucid districts in the shading naming stage, and the shading planning stage is then acquainted with additional tweak the tones in each intelligible locale. To deal with textures ordinarily found in normal pictures, we propose another shading marking plan that bunches not just adjoining pixels with comparative force yet additionally distant pixels with comparable surface. Propelled by the knowledge into the complementary nature moved by the exceptionally contrastive areas and the smooth areas, we utilize a perfection guide to direct the joining of power progression and surface comparability limitations in the plan of our labeling calculation. Inside each sound area acquired from the shading marking stage, the shading planning is applied to produce distinctive colorization impact by allotting shadings to a couple of pixels in the district. A bunch of natural interface apparatuses is intended for marking, shading and changing the outcome. We show convincing consequences of colorizing assuming measure of client input.


Full Text:

PDF

References


Landage, J., & Wankhade, M. P. (2013). Malware and malware detection techniques: A survey. International Journal of Engineering Research and Technology (IJERT), 2(12), 2278-0181.

Singhal, P., & Raul, N. (2012). Malware detection module using machine learning algorithms to assist in centralized security in enterprise networks. arXiv preprint arXiv:1205.3062.

Choudhary, S., & Sharma, A. (2020, February). Malware detection & classification using machine learning. In 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) (pp. 1-4). IEEE.

Rathore, H., Agarwal, S., Sahay, S. K., & Sewak, M. (2018, December). Malware detection using machine learning and deep learning. In International Conference on Big Data Analytics (pp. 402-411). Springer, Cham.

Namita, & Prachi. (2020). PE File-Based Malware Detection Using Machine Learning. In Proceedings of International Conference on Artificial Intelligence and Applications (113–123). Springer Singapore. https://doi.org/10.1007/978-981-15-4992-2_12

Poudyal, S., Gupta, K. D., & Sen, S. (2019). PEFile analysis: a static approach to ransomware analysis. Int J Forens Comput Sci, 1, 34-39.

Zhao, J., Zhang, S., Liu, B., & Cui, B. (2018, July). Malware detection using machine learning based on the combination of dynamic and static features. In 2018 27th International Conference on Computer Communication and Networks (ICCCN) (pp. 1-6). IEEE..

Baptista, I., Shiaeles, S., & Kolokotronis, N. (2019, May). A novel malware detection system based on machine learning and binary visualization. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1-6). IEEE.

Steinwart, I., & Christmann, A. (2008). Support vector machines. Springer Science & Business Media.

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Special topics: Exact methods for logistic regression models, Chapter 10 (10.3). Applied Logistic Regression. 3rd ed. Hoboken, NJ: John Willey & Sons, 387-394.


Refbacks

  • There are currently no refbacks.