An Adaptive Correlation Based Video Data Mining using Machine Learning
Mallikharjuna Lingam K1, VSK Reddy2
1Mallikharjuna Lingam K: Research Scholar, Faculty of Engineering, Lincoln University College, Malaysia
2VSK Reddy: Professor, Faculty of Engineering, Lincoln University College, Malaysia.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11066-11062 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5437118419/2019©BEIESP | DOI: 10.35940/ijrte.D5437.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the immense growth in the multimedia contents for education and other purposes, the availability of the video contents has also increased Nevertheless, the retrieval of the content is always a challenge. The identification of two video contents based on internal content similarity highly depends on extraction of key frames and that makes the process highly time complex. In the recent time, many of research attempts have tried to approach this problem with the intention to reduce the time complexity using various methods such as video to text conversion and further analysing both extracted text similarity analysis. Regardless to mention, this strategy is again language dependent and criticised for various reasons like local language dependencies and language paraphrase dependencies. Henceforth, this work approaches the problem with a different dimension with reduction possibilities of the video key frames using adaptive similarity. The proposed method analyses the key frames extracted from the library content and from the search video data based on various parameters and reduces the key frames using adaptive similarity. Also, this work uses machine learning and parallel programming algorithms to reduce the time complexity to a greater extend. The final outcome of this work is a reduced time complex algorithm for video data-based search to video content retrieval. The work demonstrates a nearly 50% reduction in the key frame without losing information with nearly 70% reduction in time complexity and 100% accuracy on search results.
Keywords: Video Data Mining, Key Frame Reduction, Adaptive Similarity, Video Retrieval, Multiple Dataset Performance.
Scope of the Article: Machine Learning.