Large scale incremental web video categorization

With the advent of video sharing websites, the amount of videos on the internet grows rapidly. Web video categorization is an efficient methodology for organizing the huge amount of videos. In this paper we investigate the characteristics of web videos, and make two contributions for the large scale incremental web video categorization. First, we develop an effective semantic feature space Concept Collection for Web Video with Categorization Distinguishability (CCWV-CD), which is consisted of concepts with small semantic gap, and the concept correlations are diffused by a novel Wikipedia Propagation (WP) method. Second, we propose an incremental support vector machine with fixed number of support vectors (n-ISVM) for large scale incremental learning. To evaluate the performance of CCWV-CD, WP and n-ISVM, we conduct extensive experiments on the dataset of 80,021 most representative videos on a video sharing website. The experiment results show that the CCWV-CD and WP is more representative for web videos, and the n-ISVM algorithm greatly improves the efficiency in the situation of incremental learning.