ENHANCING VIDEO RETRIVAL THROUGH BAG-OF-FEATURES AND MACHINE LEARNING FUSION
Abstract
Abstract - Content-based retrieval involves searching for information based on its content rather than qualities. The issue of content-based video retrieval (CBVR) is designing systems that can accurately and automatically process massive amounts of diverse movies. In addition, a content-based video retrieval system requires initial frame extraction. Features are derived from video frames. Finally, select an efficient similarity/classifier metric and machine learning algorithm to obtain video results linked to your query. Video frames are classified using the Random Forest Classifier, a machine learning technique, after extracting Histogram of Oriented Gradients (HOG) characteristics. Keywords: Features derived from video frames, Content Based Video Retrieval (CBVR), Histogram of Oriented Gradients, Machine Learning, Random Forest Classifier.
How to Cite
Mr. E. Ramesh, G. Jeethendra Sairam, Iyingkaran, V Harsha Vardhan Sai. (1). ENHANCING VIDEO RETRIVAL THROUGH BAG-OF-FEATURES AND MACHINE LEARNING FUSION. International Journal Of Innovation In Engineering Research & Management UGC APPROVED NO. 48708, EFI 5.89, WORLD SCINTIFIC IF 6.33, 11(8), 77-86. Retrieved from http://journal.ijierm.co.in/index.php/ijierm/article/view/2308
Section
Articles