13,406 research outputs found
Semi-supervised model-based clustering with controlled clusters leakage
In this paper, we focus on finding clusters in partially categorized data
sets. We propose a semi-supervised version of Gaussian mixture model, called
C3L, which retrieves natural subgroups of given categories. In contrast to
other semi-supervised models, C3L is parametrized by user-defined leakage
level, which controls maximal inconsistency between initial categorization and
resulting clustering. Our method can be implemented as a module in practical
expert systems to detect clusters, which combine expert knowledge with true
distribution of data. Moreover, it can be used for improving the results of
less flexible clustering techniques, such as projection pursuit clustering. The
paper presents extensive theoretical analysis of the model and fast algorithm
for its efficient optimization. Experimental results show that C3L finds high
quality clustering model, which can be applied in discovering meaningful groups
in partially classified data
Region-based Skin Color Detection.
Skin color provides a powerful cue for complex computer vision applications. Although skin color detection
has been an active research area for decades, the mainstream technology is based on the individual pixels.
This paper presents a new region-based technique for skin color detection which outperforms the current
state-of-the-art pixel-based skin color detection method on the popular Compaq dataset (Jones and Rehg,
2002). Color and spatial distance based clustering technique is used to extract the regions from the images,
also known as superpixels. In the first step, our technique uses the state-of-the-art non-parametric pixel-based
skin color classifier (Jones and Rehg, 2002) which we call the basic skin color classifier. The pixel-based skin
color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF)
is applied to further improve the results. As CRF operates over superpixels, the computational overhead is
minimal. Our technique achieves 91.17% true positive rate with 13.12% false negative rate on the Compaq
dataset tested over approximately 14,000 web images
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