16,379 research outputs found
K-Means, Mean Shift, and SLIC Clustering Algorithms: A Comparison of Performance in Color-based Skin Segmentation
Commonly used in computer vision, segmentation is grouping pixels into meaningful or perceptually similar regions. In this work, we are going to evaluate the performance of three popular data-clustering algorithms, the K-means, mean shift and SLIC algorithms, in the segmentation of human skin based on color.
The K-means algorithm Iteratively aims to group data samples into K clusters, where
each sample belongs to the cluster with the nearest mean. The mean shift algorithm is a non-
parametric algorithm that clusters data iteratively by finding the densest regions (clusters) in a feature space. An enhanced version of the classic K-means algorithm, the SLIC limits the search region to a
small area around the cluster reducing the algorithm complexity to be only dependent on
the number of pixels in the image. It also provides control over the compactness of the
clusters.
Color-based skin segmentation algorithms depend on both a color space at which segmentation is performed and a classification method used to determine whether a pixel is skin or non-skin. We have implemented the K-means, mean shift and
SLIC algorithms in the RGB color space to detect human skin. Our method begins
by clustering images using these algorithms and then segmenting the clustered regions
occupied by skin. Pixels in the clusters are classified as skin or non-skin using the Kovac
model.
We have evaluated the algorithms' performance on the SFA database (controlled environ-
ment) and on another database created for testing on an uncontrolled environment. The performance has been evaluated using time complexity, F1 score, recall, and precision. We have found that on average the mean shift
algorithm triumphs over the three algorithms in terms of performance while the SLIC algorithms holds an advantage being the fastest.The K-means algorithm has a good performance when the number of clusters K is between 10 and 15, whereas the mean shift algorithm has good performance when the bandwidth h is between 0.03 and 0.06. The SLIC algorithm maxes out its performance at around k = 100 and the number of clusters can be increased to K = 300 without remarkably increasing the complexity
Bandwidth selection for kernel estimation in mixed multi-dimensional spaces
Kernel estimation techniques, such as mean shift, suffer from one major
drawback: the kernel bandwidth selection. The bandwidth can be fixed for all
the data set or can vary at each points. Automatic bandwidth selection becomes
a real challenge in case of multidimensional heterogeneous features. This paper
presents a solution to this problem. It is an extension of \cite{Comaniciu03a}
which was based on the fundamental property of normal distributions regarding
the bias of the normalized density gradient. The selection is done iteratively
for each type of features, by looking for the stability of local bandwidth
estimates across a predefined range of bandwidths. A pseudo balloon mean shift
filtering and partitioning are introduced. The validity of the method is
demonstrated in the context of color image segmentation based on a
5-dimensional space
Automatic Image Segmentation by Dynamic Region Merging
This paper addresses the automatic image segmentation problem in a region
merging style. With an initially over-segmented image, in which the many
regions (or super-pixels) with homogeneous color are detected, image
segmentation is performed by iteratively merging the regions according to a
statistical test. There are two essential issues in a region merging algorithm:
order of merging and the stopping criterion. In the proposed algorithm, these
two issues are solved by a novel predicate, which is defined by the sequential
probability ratio test (SPRT) and the maximum likelihood criterion. Starting
from an over-segmented image, neighboring regions are progressively merged if
there is an evidence for merging according to this predicate. We show that the
merging order follows the principle of dynamic programming. This formulates
image segmentation as an inference problem, where the final segmentation is
established based on the observed image. We also prove that the produced
segmentation satisfies certain global properties. In addition, a faster
algorithm is developed to accelerate the region merging process, which
maintains a nearest neighbor graph in each iteration. Experiments on real
natural images are conducted to demonstrate the performance of the proposed
dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI
Color image segmentation using a spatial k-means clustering algorithm
This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm
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