2,782 research outputs found
Beat-Event Detection in Action Movie Franchises
While important advances were recently made towards temporally localizing and
recognizing specific human actions or activities in videos, efficient detection
and classification of long video chunks belonging to semantically defined
categories such as "pursuit" or "romance" remains challenging.We introduce a
new dataset, Action Movie Franchises, consisting of a collection of Hollywood
action movie franchises. We define 11 non-exclusive semantic categories -
called beat-categories - that are broad enough to cover most of the movie
footage. The corresponding beat-events are annotated as groups of video shots,
possibly overlapping.We propose an approach for localizing beat-events based on
classifying shots into beat-categories and learning the temporal constraints
between shots. We show that temporal constraints significantly improve the
classification performance. We set up an evaluation protocol for beat-event
localization as well as for shot classification, depending on whether movies
from the same franchise are present or not in the training data
Affective Music Information Retrieval
Much of the appeal of music lies in its power to convey emotions/moods and to
evoke them in listeners. In consequence, the past decade witnessed a growing
interest in modeling emotions from musical signals in the music information
retrieval (MIR) community. In this article, we present a novel generative
approach to music emotion modeling, with a specific focus on the
valence-arousal (VA) dimension model of emotion. The presented generative
model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the
subjectivity of emotion perception by the use of probability distributions.
Specifically, it learns from the emotion annotations of multiple subjects a
Gaussian mixture model in the VA space with prior constraints on the
corresponding acoustic features of the training music pieces. Such a
computational framework is technically sound, capable of learning in an online
fashion, and thus applicable to a variety of applications, including
user-independent (general) and user-dependent (personalized) emotion
recognition and emotion-based music retrieval. We report evaluations of the
aforementioned applications of AEG on a larger-scale emotion-annotated corpora,
AMG1608, to demonstrate the effectiveness of AEG and to showcase how
evaluations are conducted for research on emotion-based MIR. Directions of
future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio
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