Abstract

The explosive growth of video data poses a significant challenge in retrieving relevant content swiftly. Existing methods often fall short in providing concise yet informative summaries and efficient retrieval mechanisms. The primary issue lies in the overwhelming volume of video data, making it cumbersome for users to identify and access pertinent information efficiently. Traditional summarization techniques lack the sophistication to capture the nuances of video content, leading to a gap in effective content retrieval. Our approach involves training a Deep Belief Network (DBN) to autonomously generate concise yet comprehensive video summaries. Simultaneously, the Radial Basis Function (RBF) is employed to develop an efficient content retrieval system, leveraging the learned features from the video summarization process. The integration of these two methods promises a novel and effective solution to the challenges posed by the burgeoning volume of video content. Preliminary results demonstrate a significant improvement in the efficiency of content retrieval, with the integrated DBN and RBF approach outperforming traditional methods. The video summaries generated by the DBN exhibit enhanced informativeness, contributing to more accurate and rapid content retrieval.

Authors

Kaavya Kanagaraj¹, Shilpa Abhang², Julakanti Sampath Kumar³, R.K. Gnanamurthy⁴, V. Balaji⁵
SRM Institute of Science and Engineering, Kattankulathur Campus, India^1,2,3,4, MAI-NEFHI College of Engineering and Technology Asmara, Eritera⁵

Keywords

Video Summarization, DBN, Content Retrieval, RBF, Multimedia Content