Feature based-Learning with Data Increasing for video Recommendation and Computing

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Published: Aug 8, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6.7787
Keywords:
Feature re-learning, ranking loss, data augmentation, content-based video recommendation

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Ramakrishna Reddy K
Research Scholar, Department of Computer Science and Engineering, Bhagwant University,Rajasthan-India
B. Dhanasekaran
Research Supervisor, Department of Computer Science and Engineering, Bhagwant University, Rajasthan-India
V. K. Sharma
Research Co-Supervisor, Department of EEE, Bhagwant University, Rajasthan-India

Abstract

Image content analysis is crucial for determining the reliability of a link between two videos. Video characteristics are increasingly being used in image and video representation as custom pre-trained picture and video convolutional neural networks become generally available. People also have limited access to video editing tools for a variety of reasons, such as ownership and privacy concerns. You don't need to go back to the source video data to use the refined features again. An affine transformation, for instance, can be used to map a well-studied function onto an unfamiliar domain. To do this, we use a unique triplet failure in conjunction with the re-learning strategy. We propose a contemporary data augmentation method that may be applied to functionality on various frames for videos as an alternative to employing specific motion data. Extensive testing on the well-known Hulu content-based Video Relevance challenge demonstrates the process's efficacy and provides solid evidence of state-of-the-art performance.

Article Details

How to Cite
Reddy K, R. ., Dhanasekaran, B. ., & Sharma, V. K. . (2023). Feature based-Learning with Data Increasing for video Recommendation and Computing. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6), 484–490. https://doi.org/10.17762/ijritcc.v11i6.7787
Issue
Vol. 11 No. 6 (2023): June (2023) Issue
Section
Articles

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