A Systematic Video Indexing Approach Using Decision Tree

Abstract

A systematic categorization approach for video indexing is presented in this paper. The amount of multimedia information that can be accessed over the internet continues to expand exponentially. Due to this growth and development of multimedia on the internet, particularly videos, there has been a rise in the need for video retrieval. The goal of this work is to discover subsets of characteristics that are suitable for indexing or categorizing video content. The features chosen from the frames of the video have dominant texture characteristics. Several statistical features have been applied for better performance with decision tree classification. The investigation included 1000 videos from different video contents, such as news, cartoon, advertisements, movies, and sports categories. Results show that the overall misclassification rate percentage was below 3%. The capability of indexing the video contents indicates the real power of the proposed system, which can enhance existing indexing services, thereby enriching the tools that are available for video indexing.

Efficient deep learning architecture for the classification of diseased plant leaves

“…This has made it possible to respond to outbreaks in a manner that is both faster and more accurate. With the help of traditional classification systems like k-nearest neighbor [1], decision trees [2], support vector machine (SVM) [1], and neural networks [3], a broad range of automated approaches have been developed. The accuracy of traditional classification systems is reliant on the features that are extracted (colour and textures) as well as the classifiers that are chosen.…”