A Study on the Application of New Feature Techniques for Multimedia Analysis in Artificial Neural Networks by Fusing Image Processing

Abstract

 To evaluate and extract information from multimedia material including photos, videos, and audio, a fusion of image processing and computer vision methods known as kernel principal component analysis (KPCA) is used. The objective is to create a system that can automatically identify relevant aspects of multimedia data and make them available for analysis and decision-making. By merging various processing methods, the fusion of image processing and multimedia analysis may improve analysis efficiency. The CT scans of one thousand Chinese hospital patients are included in the large TianChi contest dataset used to train artificial neural networks (ANN) for use in multimedia analysis. For multimedia analysis by combining image processing, we proposed kernel principal component analysis with artificial neural networks (KPCA-ANN) in this paper. The ability to process, analyze, and understand multimedia data by merging image processing into multimedia analysis has tremendous promise. It may improve decision-making, deepen our comprehension of complicated processes, and provide more fruitful means of information exchange. The experimental findings demonstrate that the proposed strategy has provided a absolute mean error of 7 and structural similarity index of 86.