9,526 research outputs found
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
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Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
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Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
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First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
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Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
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Third, we add the functionality to process the users' relevance feedback.\ud
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We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
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We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
Content-based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events
As amounts of publicly available video data grow the need to query this data efficiently becomes significant. Consequently content-based retrieval of video data turns out to be a challenging and important problem. We address the specific aspect of inferring semantics automatically from raw video data. In particular, we introduce a new video data model that supports the integrated use of two different approaches for mapping low-level features to high-level concepts. Firstly, the model is extended with a rule-based approach that supports spatio-temporal formalization of high-level concepts, and then with a stochastic approach. Furthermore, results on real tennis video data are presented, demonstrating the validity of both approaches, as well us advantages of their integrated us
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
We present the Bayesian Case Model (BCM), a general framework for Bayesian
case-based reasoning (CBR) and prototype classification and clustering. BCM
brings the intuitive power of CBR to a Bayesian generative framework. The BCM
learns prototypes, the "quintessential" observations that best represent
clusters in a dataset, by performing joint inference on cluster labels,
prototypes and important features. Simultaneously, BCM pursues sparsity by
learning subspaces, the sets of features that play important roles in the
characterization of the prototypes. The prototype and subspace representation
provides quantitative benefits in interpretability while preserving
classification accuracy. Human subject experiments verify statistically
significant improvements to participants' understanding when using explanations
produced by BCM, compared to those given by prior art.Comment: Published in Neural Information Processing Systems (NIPS) 2014,
Neural Information Processing Systems (NIPS) 201
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Originally designed to model text, topic modeling has become a powerful tool
for uncovering latent structure in domains including medicine, finance, and
vision. The goals for the model vary depending on the application: in some
cases, the discovered topics may be used for prediction or some other
downstream task. In other cases, the content of the topic itself may be of
intrinsic scientific interest.
Unfortunately, even using modern sparse techniques, the discovered topics are
often difficult to interpret due to the high dimensionality of the underlying
space. To improve topic interpretability, we introduce Graph-Sparse LDA, a
hierarchical topic model that leverages knowledge of relationships between
words (e.g., as encoded by an ontology). In our model, topics are summarized by
a few latent concept-words from the underlying graph that explain the observed
words. Graph-Sparse LDA recovers sparse, interpretable summaries on two
real-world biomedical datasets while matching state-of-the-art prediction
performance
Inference and Evaluation of the Multinomial Mixture Model for Text Clustering
In this article, we investigate the use of a probabilistic model for
unsupervised clustering in text collections. Unsupervised clustering has become
a basic module for many intelligent text processing applications, such as
information retrieval, text classification or information extraction. The model
considered in this contribution consists of a mixture of multinomial
distributions over the word counts, each component corresponding to a different
theme. We present and contrast various estimation procedures, which apply both
in supervised and unsupervised contexts. In supervised learning, this work
suggests a criterion for evaluating the posterior odds of new documents which
is more statistically sound than the "naive Bayes" approach. In an unsupervised
context, we propose measures to set up a systematic evaluation framework and
start with examining the Expectation-Maximization (EM) algorithm as the basic
tool for inference. We discuss the importance of initialization and the
influence of other features such as the smoothing strategy or the size of the
vocabulary, thereby illustrating the difficulties incurred by the high
dimensionality of the parameter space. We also propose a heuristic algorithm
based on iterative EM with vocabulary reduction to solve this problem. Using
the fact that the latent variables can be analytically integrated out, we
finally show that Gibbs sampling algorithm is tractable and compares favorably
to the basic expectation maximization approach
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