8,173 research outputs found
A Deep Siamese Network for Scene Detection in Broadcast Videos
We present a model that automatically divides broadcast videos into coherent
scenes by learning a distance measure between shots. Experiments are performed
to demonstrate the effectiveness of our approach by comparing our algorithm
against recent proposals for automatic scene segmentation. We also propose an
improved performance measure that aims to reduce the gap between numerical
evaluation and expected results, and propose and release a new benchmark
dataset.Comment: ACM Multimedia 201
Who is the director of this movie? Automatic style recognition based on shot features
We show how low-level formal features, such as shot duration, meant as length
of camera takes, and shot scale, i.e. the distance between the camera and the
subject, are distinctive of a director's style in art movies. So far such
features were thought of not having enough varieties to become distinctive of
an author. However our investigation on the full filmographies of six different
authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total
number of 120 movies analysed second by second, confirms that these
shot-related features do not appear as random patterns in movies from the same
director. For feature extraction we adopt methods based on both conventional
and deep learning techniques. Our findings suggest that feature sequential
patterns, i.e. how features evolve in time, are at least as important as the
related feature distributions. To the best of our knowledge this is the first
study dealing with automatic attribution of movie authorship, which opens up
interesting lines of cross-disciplinary research on the impact of style on the
aesthetic and emotional effects on the viewers
Modelling of content-aware indicators for effective determination of shot boundaries in compressed MPEG videos
In this paper, a content-aware approach is proposed to design multiple test conditions for shot cut detection, which are organized into a multiple phase decision tree for abrupt cut detection and a finite state machine for dissolve detection. In comparison with existing approaches, our algorithm is characterized with two categories of content difference indicators and testing. While the first category indicates the content changes that are directly used for shot cut detection, the second category indicates the contexts under which the content change occurs. As a result, indications of frame differences are tested with context awareness to make the detection of shot cuts adaptive to both content and context changes. Evaluations announced by TRECVID 2007 indicate that our proposed algorithm achieved comparable performance to those using machine learning approaches, yet using a simpler feature set and straightforward design strategies. This has validated the effectiveness of modelling of content-aware indicators for decision making, which also provides a good alternative to conventional approaches in this topic
A COMPUTATION METHOD/FRAMEWORK FOR HIGH LEVEL VIDEO CONTENT ANALYSIS AND SEGMENTATION USING AFFECTIVE LEVEL INFORMATION
VIDEO segmentation facilitates e±cient video indexing and navigation in large
digital video archives. It is an important process in a content-based video
indexing and retrieval (CBVIR) system. Many automated solutions performed seg-
mentation by utilizing information about the \facts" of the video. These \facts"
come in the form of labels that describe the objects which are captured by the cam-
era. This type of solutions was able to achieve good and consistent results for some
video genres such as news programs and informational presentations. The content
format of this type of videos is generally quite standard, and automated solutions
were designed to follow these format rules. For example in [1], the presence of news
anchor persons was used as a cue to determine the start and end of a meaningful
news segment.
The same cannot be said for video genres such as movies and feature films.
This is because makers of this type of videos utilized different filming techniques to
design their videos in order to elicit certain affective response from their targeted
audience. Humans usually perform manual video segmentation by trying to relate
changes in time and locale to discontinuities in meaning [2]. As a result, viewers
usually have doubts about the boundary locations of a meaningful video segment
due to their different affective responses.
This thesis presents an entirely new view to the problem of high level video
segmentation. We developed a novel probabilistic method for affective level video
content analysis and segmentation. Our method had two stages. In the first stage,
a®ective content labels were assigned to video shots by means of a dynamic bayesian
0. Abstract 3
network (DBN). A novel hierarchical-coupled dynamic bayesian network (HCDBN)
topology was proposed for this stage. The topology was based on the pleasure-
arousal-dominance (P-A-D) model of a®ect representation [3]. In principle, this
model can represent a large number of emotions. In the second stage, the visual,
audio and a®ective information of the video was used to compute a statistical feature
vector to represent the content of each shot. Affective level video segmentation was
achieved by applying spectral clustering to the feature vectors.
We evaluated the first stage of our proposal by comparing its emotion detec-
tion ability with all the existing works which are related to the field of a®ective video
content analysis. To evaluate the second stage, we used the time adaptive clustering
(TAC) algorithm as our performance benchmark. The TAC algorithm was the best
high level video segmentation method [2]. However, it is a very computationally
intensive algorithm. To accelerate its computation speed, we developed a modified
TAC (modTAC) algorithm which was designed to be mapped easily onto a field
programmable gate array (FPGA) device. Both the TAC and modTAC algorithms
were used as performance benchmarks for our proposed method.
Since affective video content is a perceptual concept, the segmentation per-
formance and human agreement rates were used as our evaluation criteria. To obtain
our ground truth data and viewer agreement rates, a pilot panel study which was
based on the work of Gross et al. [4] was conducted. Experiment results will show
the feasibility of our proposed method. For the first stage of our proposal, our
experiment results will show that an average improvement of as high as 38% was
achieved over previous works. As for the second stage, an improvement of as high
as 37% was achieved over the TAC algorithm
Highly efficient low-level feature extraction for video representation and retrieval.
PhDWitnessing the omnipresence of digital video media, the research community has
raised the question of its meaningful use and management. Stored in immense
multimedia databases, digital videos need to be retrieved and structured in an
intelligent way, relying on the content and the rich semantics involved. Current
Content Based Video Indexing and Retrieval systems face the problem of the semantic
gap between the simplicity of the available visual features and the richness of user
semantics.
This work focuses on the issues of efficiency and scalability in video indexing and
retrieval to facilitate a video representation model capable of semantic annotation. A
highly efficient algorithm for temporal analysis and key-frame extraction is developed.
It is based on the prediction information extracted directly from the compressed domain
features and the robust scalable analysis in the temporal domain. Furthermore,
a hierarchical quantisation of the colour features in the descriptor space is presented.
Derived from the extracted set of low-level features, a video representation model that
enables semantic annotation and contextual genre classification is designed.
Results demonstrate the efficiency and robustness of the temporal analysis algorithm
that runs in real time maintaining the high precision and recall of the detection task.
Adaptive key-frame extraction and summarisation achieve a good overview of the
visual content, while the colour quantisation algorithm efficiently creates hierarchical
set of descriptors. Finally, the video representation model, supported by the genre
classification algorithm, achieves excellent results in an automatic annotation system by
linking the video clips with a limited lexicon of related keywords
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