2,894 research outputs found
A D.C. Programming Approach to the Sparse Generalized Eigenvalue Problem
In this paper, we consider the sparse eigenvalue problem wherein the goal is
to obtain a sparse solution to the generalized eigenvalue problem. We achieve
this by constraining the cardinality of the solution to the generalized
eigenvalue problem and obtain sparse principal component analysis (PCA), sparse
canonical correlation analysis (CCA) and sparse Fisher discriminant analysis
(FDA) as special cases. Unlike the -norm approximation to the
cardinality constraint, which previous methods have used in the context of
sparse PCA, we propose a tighter approximation that is related to the negative
log-likelihood of a Student's t-distribution. The problem is then framed as a
d.c. (difference of convex functions) program and is solved as a sequence of
convex programs by invoking the majorization-minimization method. The resulting
algorithm is proved to exhibit \emph{global convergence} behavior, i.e., for
any random initialization, the sequence (subsequence) of iterates generated by
the algorithm converges to a stationary point of the d.c. program. The
performance of the algorithm is empirically demonstrated on both sparse PCA
(finding few relevant genes that explain as much variance as possible in a
high-dimensional gene dataset) and sparse CCA (cross-language document
retrieval and vocabulary selection for music retrieval) applications.Comment: 40 page
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
TagBook: A Semantic Video Representation without Supervision for Event Detection
We consider the problem of event detection in video for scenarios where only
few, or even zero examples are available for training. For this challenging
setting, the prevailing solutions in the literature rely on a semantic video
representation obtained from thousands of pre-trained concept detectors.
Different from existing work, we propose a new semantic video representation
that is based on freely available social tagged videos only, without the need
for training any intermediate concept detectors. We introduce a simple
algorithm that propagates tags from a video's nearest neighbors, similar in
spirit to the ones used for image retrieval, but redesign it for video event
detection by including video source set refinement and varying the video tag
assignment. We call our approach TagBook and study its construction,
descriptiveness and detection performance on the TRECVID 2013 and 2014
multimedia event detection datasets and the Columbia Consumer Video dataset.
Despite its simple nature, the proposed TagBook video representation is
remarkably effective for few-example and zero-example event detection, even
outperforming very recent state-of-the-art alternatives building on supervised
representations.Comment: accepted for publication as a regular paper in the IEEE Transactions
on Multimedi
Retrieval and Annotation of Music Using Latent Semantic Models
PhDThis thesis investigates the use of latent semantic models for annotation and
retrieval from collections of musical audio tracks. In particular latent semantic
analysis (LSA) and aspect models (or probabilistic latent semantic analysis,
pLSA) are used to index words in descriptions of music drawn from hundreds
of thousands of social tags. A new discrete audio feature representation is introduced
to encode musical characteristics of automatically-identified regions
of interest within each track, using a vocabulary of audio muswords. Finally a
joint aspect model is developed that can learn from both tagged and untagged
tracks by indexing both conventional words and muswords. This model is
used as the basis of a music search system that supports query by example and
by keyword, and of a simple probabilistic machine annotation system. The
models are evaluated by their performance in a variety of realistic retrieval
and annotation tasks, motivated by applications including playlist generation,
internet radio streaming, music recommendation and catalogue searchEngineering and Physical Sciences
Research Counci
Evolution of Information Retrieval System: Critical Review of Multimedia Information Retrieval System Based On Content, Context, and Concept
In recent years the explosive growth of information affects the flood of information. The amount of information must be followed by the development of the effective Information Retrieval System (IRS) so that the information will be easily accessible and useful for the user. The source of Information contains various media format, beside text there is also image, audio, and video that called multimedia. A large number of multimedia information rise the Multimedia Information Retrieval System (MIRS). Most of MIRS today is monolithic or only using one media format like Google1 for text search, tineye2 for image search, youtube3 for video search or 4shared4 for music and audio search. There is a need of information in any kind of media, not only retrieve the document in text format, but also retrieve the document in an image, audio and video format at once from any kind media format of the query. This study reviews the evolution of IRS, regress from text-based to concept- based MIRS. Unified Multimedia Indexing technique is discussed along with Concept-based MIRS. This critical review concludes that the evolution of IRS follows three paces: content-based, context-based and concept-based. Each pace takes on indexing system and retrieval techniques to optimize information retrieved. The challenge is how to come up with a retrieval technique that can process unified MIRS in order to retrieve optimally the relevant document
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