Shot genre classification using compressed audio-visual features
Masaru Sugano, Roger Isaksson, Yasuyuki Nakajima, Hiromasa Yanagihara · 2004
This paper proposes shot genre classification from MPEG compressed movies, as one of the high-level indexing methods for audio-visual contents. Through statistical analysis of low-level and mid-level audio-visual features on compressed domain, the proposed method can achieve subjectively accurate shot classification within the movies into predefined genre set, which can be applied to various content handling applications, such as summarization, navigation, editing, filtering, and so on. By feeding subjectively evaluated feature set for each shot genre into the linear machine decision tree classifier, each shot is classified at very low cost. The experimental results show that most of the shots in the movies can be classified into subjectively accurate genres, and also the dominant shot genre can correctly resolve each movie genre.