13,473 research outputs found
K Means Segmentation of Alzheimers Disease in PET scan datasets: An implementation
The Positron Emission Tomography (PET) scan image requires expertise in the
segmentation where clustering algorithm plays an important role in the
automation process. The algorithm optimization is concluded based on the
performance, quality and number of clusters extracted. This paper is proposed
to study the commonly used K Means clustering algorithm and to discuss a brief
list of toolboxes for reproducing and extending works presented in medical
image analysis. This work is compiled using AForge .NET framework in windows
environment and MATrix LABoratory (MATLAB 7.0.1)Comment: International Joint Conference on Advances in Signal Processing and
Information Technology, SPIT201
Confidence-guided Centroids for Unsupervised Person Re-Identification
Unsupervised person re-identification (ReID) aims to train a feature
extractor for identity retrieval without exploiting identity labels. Due to the
blind trust in imperfect clustering results, the learning is inevitably misled
by unreliable pseudo labels. Albeit the pseudo label refinement has been
investigated by previous works, they generally leverage auxiliary information
such as camera IDs and body part predictions. This work explores the internal
characteristics of clusters to refine pseudo labels. To this end,
Confidence-Guided Centroids (CGC) are proposed to provide reliable cluster-wise
prototypes for feature learning. Since samples with high confidence are
exclusively involved in the formation of centroids, the identity information of
low-confidence samples, i.e., boundary samples, are NOT likely to contribute to
the corresponding centroid. Given the new centroids, current learning scheme,
where samples are enforced to learn from their assigned centroids solely, is
unwise. To remedy the situation, we propose to use Confidence-Guided pseudo
Label (CGL), which enables samples to approach not only the originally assigned
centroid but other centroids that are potentially embedded with their identity
information. Empowered by confidence-guided centroids and labels, our method
yields comparable performance with, or even outperforms, state-of-the-art
pseudo label refinement works that largely leverage auxiliary information
Speeding Up MCMC by Efficient Data Subsampling
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework
where the likelihood function for observations is estimated from a random
subset of observations. We introduce a highly efficient unbiased estimator
of the log-likelihood based on control variates, such that the computing cost
is much smaller than that of the full log-likelihood in standard MCMC. The
likelihood estimate is bias-corrected and used in two dependent pseudo-marginal
algorithms to sample from a perturbed posterior, for which we derive the
asymptotic error with respect to and , respectively. We propose a
practical estimator of the error and show that the error is negligible even for
a very small in our applications. We demonstrate that Subsampling MCMC is
substantially more efficient than standard MCMC in terms of sampling efficiency
for a given computational budget, and that it outperforms other subsampling
methods for MCMC proposed in the literature.Comment: Main changes: The theory has been significantly revise
- …