9,638 research outputs found
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
We examine a network of learners which address the same classification task
but must learn from different data sets. The learners cannot share data but
instead share their models. Models are shared only one time so as to preserve
the network load. We introduce DELCO (standing for Decentralized Ensemble
Learning with COpulas), a new approach allowing to aggregate the predictions of
the classifiers trained by each learner. The proposed method aggregates the
base classifiers using a probabilistic model relying on Gaussian copulas.
Experiments on logistic regressor ensembles demonstrate competing accuracy and
increased robustness in case of dependent classifiers. A companion python
implementation can be downloaded at https://github.com/john-klein/DELC
Bayesian Classifier Fusion with an Explicit Model of Correlation
Combining the outputs of multiple classifiers or experts into a single
probabilistic classification is a fundamental task in machine learning with
broad applications from classifier fusion to expert opinion pooling. Here we
present a hierarchical Bayesian model of probabilistic classifier fusion based
on a new correlated Dirichlet distribution. This distribution explicitly models
positive correlations between marginally Dirichlet-distributed random vectors
thereby allowing explicit modeling of correlations between base classifiers or
experts. The proposed model naturally accommodates the classic Independent
Opinion Pool and other independent fusion algorithms as special cases. It is
evaluated by uncertainty reduction and correctness of fusion on synthetic and
real-world data sets. We show that a change in performance of the fused
classifier due to uncertainty reduction can be Bayes optimal even for highly
correlated base classifiers.Comment: 12 pages, 4 figures, 1 table, revised title and Fig 2, added real
data set Bookies
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
Automatic object classification for surveillance videos.
PhDThe recent popularity of surveillance video systems, specially located in urban
scenarios, demands the development of visual techniques for monitoring purposes.
A primary step towards intelligent surveillance video systems consists on automatic
object classification, which still remains an open research problem and the keystone
for the development of more specific applications.
Typically, object representation is based on the inherent visual features. However,
psychological studies have demonstrated that human beings can routinely categorise
objects according to their behaviour. The existing gap in the understanding
between the features automatically extracted by a computer, such as appearance-based
features, and the concepts unconsciously perceived by human beings but
unattainable for machines, or the behaviour features, is most commonly known
as semantic gap. Consequently, this thesis proposes to narrow the semantic gap
and bring together machine and human understanding towards object classification.
Thus, a Surveillance Media Management is proposed to automatically detect and
classify objects by analysing the physical properties inherent in their appearance
(machine understanding) and the behaviour patterns which require a higher level of
understanding (human understanding). Finally, a probabilistic multimodal fusion
algorithm bridges the gap performing an automatic classification considering both
machine and human understanding.
The performance of the proposed Surveillance Media Management framework
has been thoroughly evaluated on outdoor surveillance datasets. The experiments
conducted demonstrated that the combination of machine and human understanding
substantially enhanced the object classification performance. Finally, the inclusion
of human reasoning and understanding provides the essential information to bridge
the semantic gap towards smart surveillance video systems
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