55,359 research outputs found
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Using supervised machine learning approaches to recognize human activities
from on-body wearable accelerometers generally requires a large amount of
labelled data. When ground truth information is not available, too expensive,
time consuming or difficult to collect, one has to rely on unsupervised
approaches. This paper presents a new unsupervised approach for human activity
recognition from raw acceleration data measured using inertial wearable
sensors. The proposed method is based upon joint segmentation of
multidimensional time series using a Hidden Markov Model (HMM) in a multiple
regression context. The model is learned in an unsupervised framework using the
Expectation-Maximization (EM) algorithm where no activity labels are needed.
The proposed method takes into account the sequential appearance of the data.
It is therefore adapted for the temporal acceleration data to accurately detect
the activities. It allows both segmentation and classification of the human
activities. Experimental results are provided to demonstrate the efficiency of
the proposed approach with respect to standard supervised and unsupervised
classification approache
CUTS: A Fully Unsupervised Framework for Medical Image Segmentation
In this work we introduce CUTS (Contrastive and Unsupervised Training for
Segmentation) the first fully unsupervised deep learning framework for medical
image segmentation, facilitating the use of the vast majority of imaging data
that is not labeled or annotated. Segmenting medical images into regions of
interest is a critical task for facilitating both patient diagnoses and
quantitative research. A major limiting factor in this segmentation is the lack
of labeled data, as getting expert annotations for each new set of imaging data
or task can be expensive, labor intensive, and inconsistent across annotators:
thus, we utilize self-supervision based on pixel-centered patches from the
images themselves. Our unsupervised approach is based on a training objective
with both contrastive learning and autoencoding aspects. Previous contrastive
learning approaches for medical image segmentation have focused on image-level
contrastive training, rather than our intra-image patch-level approach or have
used this as a pre-training task where the network needed further supervised
training afterwards. By contrast, we build the first entirely unsupervised
framework that operates at the pixel-centered-patch level. Specifically, we add
novel augmentations, a patch reconstruction loss, and introduce a new pixel
clustering and identification framework. Our model achieves improved results on
several key medical imaging tasks, as verified by held-out expert annotations
on the task of segmenting geographic atrophy (GA) regions of images of the
retina
Unsupervised colour image segmentation by low-level perceptual grouping
This paper proposes a new unsupervised
approach for colour image segmentation. A hierarchy of
image partitions is created on the basis of a function that
merges spatially connected regions according to primary
perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to
choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with
five unsupervised colour image segmentation techniques.
These techniques have been frequently used as a reference
in other comparisons. The results obtained by each method
have been systematically evaluated using four well-known
unsupervised measures for judging the segmentation
quality. Our methodology has globally shown the best
performance, obtaining better results in three out of four of
these segmentation quality measures. Experiments will also
show that our proposal finds low-level perceptual solutions
that are highly correlated with the ones provided by
human
Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space
In this report, we propose a novel and efficient approach for active unsurpervised texture segmentation. First, we show how we can extract a small set of good features for texture segmentation based on the structure tensor and nonlinear diffusion. Then, we propose a variational framework that allows to incorporate these features in a level set based unsupervised segmentation process that adaptively takes into account their estimated statistical information inside and outside the region to segment. Unlike features obtained by Gabor filters, our approach naturally leads to a significantly reduced number of feature channels. Thus, the supervised part of a texture segmentation algorithm, where the choice of good feature channels has to be learned in advance, can be omitted, and we get an efficient solution for unsupervised texture segmentation. The actual segmentation process based on the new features is an active and adaptative contour model that estimates dynamically probability density functions inside and outside a region and produces very convincing results. It is implemented using a fast level set based active contour technique and has been tested on various real textured images. The performance of the approach is favorably compared to recent studies
Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes
Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought.Peer Reviewe
Bayesian weighted K-Means clustering algorithm as applied to cotton trash measurement
Image segmentation is one of most difficult tasks in computer vision. It plays a critical role in object recognition of natural images. Unsupervised classification, or clustering, represents one promising approach for solving the image segmentation problem in typical application environments. The K-Means and Bayesian Learning algorithms are two well-known unsupervised classification methods. The K-Means approach is computationally efficient, but assumes imaging conditions which are unrealistic in many practical applications. While the Bayesian learning technique always produces a theoretically optimal segmentation result, the large, computational burden it requires is often unacceptable for many industrial tasks. A novel clustering algorithm, called Bayesian Weighted K-Means, is proposed in this thesis. Through simplification of the Bayesian learning approach\u27s decision-making process using cluster weights, the new technique is able to provide approximately optimal segmentation results while maintaining the computational efficiency generally associated with the K-means algorithm. The capabilities of this new algorithm are demonstrated using both synthetic images with controlled amounts of noise, and real color images of cotton lint contaminated with non-lint material
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