9,882 research outputs found
The aceToolbox: low-level audiovisual feature extraction for retrieval and classification
In this paper we present an overview of a software platform
that has been developed within the aceMedia project,
termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM),
with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images
Multi-level Semantic Analysis for Sports Video
There has been a huge increase in the utilization of video as one of the most preferred type of media due to its content richness for many significant applications including sports. To sustain an ongoing rapid growth of sports video, there is an emerging demand for a sophisticated content-based indexing system. Users recall video contents in a high-level abstraction while video is generally stored as an arbitrary sequence of audio-visual tracks. To bridge this gap, this paper will demonstrate the use of domain knowledge and characteristics to design the extraction of high-level concepts directly from audio-visual features. In particular, we propose a multi-level semantic analysis framework to optimize the sharing of domain characteristics
Event detection in field sports video using audio-visual features and a support vector machine
In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable
A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification
One of the biggest challenges of acoustic scene classification (ASC) is to
find proper features to better represent and characterize environmental sounds.
Environmental sounds generally involve more sound sources while exhibiting less
structure in temporal spectral representations. However, the background of an
acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting
it could be characterized by distribution statistics rather than temporal
details. In this work, we investigated using auditory summary statistics as the
feature for ASC tasks. The inspiration comes from a recent neuroscience study,
which shows the human auditory system tends to perceive sound textures through
time-averaged statistics. Based on these statistics, we further proposed to use
linear discriminant analysis to eliminate redundancies among these statistics
while keeping the discriminative information, providing an extreme com-pact
representation for acoustic scenes. Experimental results show the outstanding
performance of the proposed feature over the conventional handcrafted features.Comment: Accepted as a conference paper of Interspeech 201
Extensible Detection and Indexing of Highlight Events in Broadcasted Sports Video
Content-based indexing is fundamental to support and sustain the ongoing growth of broadcasted sports video. The main challenge is to design extensible frameworks to detect and index highlight events. This paper presents: 1) A statistical-driven event detection approach that utilizes a minimum amount of manual knowledge and is based on a universal scope-of-detection and audio-visual features; 2) A semi-schema-based indexing that combines the benefits of schema-based modeling to ensure that the video indexes are valid at all time without manual checking, and schema-less modeling to allow several passes of instantiation in which additional elements can be declared. To demonstrate the performance of the events detection, a large dataset of sport videos with a total of around 15 hours including soccer, basketball and Australian football is used
Sentiment Recognition in Egocentric Photostreams
Lifelogging is a process of collecting rich source of information about daily
life of people. In this paper, we introduce the problem of sentiment analysis
in egocentric events focusing on the moments that compose the images recalling
positive, neutral or negative feelings to the observer. We propose a method for
the classification of the sentiments in egocentric pictures based on global and
semantic image features extracted by Convolutional Neural Networks. We carried
out experiments on an egocentric dataset, which we organized in 3 classes on
the basis of the sentiment that is recalled to the user (positive, negative or
neutral)
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