Summarization of wearable videos using support vector machine
Haung Wei Ng, Yasuhito Sawahata, Kiyoharu Aizawa · 2003
Auto-summarization of video contents has become an important topic following the growing amount of multimedia contents. Researches in this area have shown the effectiveness of low-level video and audio features in categorizing video contents. The use of brainwaves to reflect personal interests is also proven to be practical. In this paper, we model the relationship between audio/video features and brainwaves (/spl alpha/-waves) using the support vector machine (SVM). Based on the SVM model, we summarized wearable videos by personal interests, using only low-level video and audio features. Here we define "wearable videos" as continuous recordings of personal experiences using wearable video camera and computer. Our experiment results showed over 90% of accuracy on summarization of a 25-minute video clip with an SVM model created by another resembling 25-minute video clip.