Enhanced Video Classification by Time Domain Features Using SVM & RB Kernel Function

Notice: Undefined variable: link_article in /home/ijtc/public_html/plugins/content/bt_socialshare/bt_socialshare.php on line 818

Notice: Undefined property: stdClass::$image in /home/ijtc/public_html/plugins/content/bt_socialshare/bt_socialshare.php on line 818

Notice: Undefined variable: title in /home/ijtc/public_html/plugins/content/bt_socialshare/bt_socialshare.php on line 818

Notice: Undefined offset: 1 in /home/ijtc/public_html/plugins/content/bt_socialshare/bt_socialshare.php on line 820
File Size:
1020.94 kB
Volume 3, Issue 6 (June, 2017)
Publication No:
Palwinder, Er.Jyoti Rani

Notice: Undefined variable: pdFileDate in /home/ijtc/public_html/components/com_phocadownload/views/file/tmpl/default.php on line 277
4 x

TODAY people have access to a tremendous amount of video, both on television and the Internet. The amount of video that a viewer has to choose from is now so large that it is infeasible for a human to go through it all to find video of interest. One method that viewers use to narrow their choices is to look for video within specific categories or genre. A large number of approaches have been attempted for performing automatic classification of video. The approaches for video classification could be divided into four groups: text-based approaches, audio-based approaches, visual-based approaches, and those that used some combination of text, audio, and visual features. This paper mainly focus on audio based approach in which audio features of dataset of videos has been collected. We propose a new method for enhancing features extraction and improving the efficiency of videos classification by SVM with radial basis Kernel function

Features Extraction; SVM; Classification; RBF; Kernels