Retrieval of Video Contents based on Deep Parameter Analysis 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 21 September 2019 | Revised Manuscript received on 06 October 2019 | Manuscript Published on 11 October 2019 | PP: 772-779 | Volume-8 Issue-2S10 September 2019 | Retrieval Number: B11380982S1019/2019©BEIESP | DOI: 10.35940/ijrte.B1138.0982S1019
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Abstract: In the recent past, video content-based communication hasincreases with a significant consumption of space and time complexity.The introduction of the data is exceedingly improved in video information as the video information incorporates visual and sound data. The mix of these two kinds of information for a single data portrayal is exceedingly compelling as the broad media substance can make an ever-increasing number of effects on the human cerebrum. Thus, most of the substance for training or business or restorative area are video-based substances. This development in video information have impacted a significant number of the professional to fabricate and populate video content library for their use. Hence, retrieval of the accurate video data is the prime task for all video content management frameworks. A good number of researches are been carried out in the field of video retrieval using various methods. Most of the parallel research outcomes have focused on content retrieval based on object classification for the video frames and further matching the object information with other video contents based on the similar information. This method is highly criticised and continuously improving as the method solely relies on fundamental object detection and classification using the preliminary characteristics. These characteristics are primarily depending on shape or colour or area of the objects and cannot be accurate for detection of similarities. Hence, this work proposes, a novel method for similarity-based retrieval of video contents using deep characteristics. The work majorly focuses on extraction of moving objects, static objects separation, motion vector analysis of the moving objects and the traditional parameters as area from the video contents and further perform matching for retrieval or extraction of the video data. The proposed novel algorithm for content retrieval demonstrates 98% accuracy with 90% reduction in time complexity.
Keywords: Object Separation, Regeneration of Regions, Moving Objects Detection, Frame of Reference Stabilization, Frame Rate Calibration.
Scope of the Article: Machine Learning