Related work
Although the research in this area has led to many different
image segmentation techniques presented in the literature [6], no
single algorithm can be considered good for all applications and all images
[7]. Segmentation methods used for color images can be
divided into two main categories: feature-space based techniques
(clustering methods [8,9,10] and histogram
multi-thresholding [7,11,12]) and
image-domain based techniques. The latter are further divided into
pixel-similarity based algorithms
[13,14,15] and pixel-difference based algorithms
[2,4,16]. Image-domain based techniques exploit the
pixel context interaction which increase computational
complexity. The most common feature-space techniques
gained their popularity through the adaptive k-means
algorithm [17,18]. Most of these methods are
parameterized with number of expected clusters. Number of
clusters is determined automatically in the Mean Shift algorithm
[1] using nonparametric density estimation and gradient guided
hill-climbing procedure.
Histogram-based approaches relay on the estimation of the discrete density in
the designated color space where the clusters
are represented by hills in the histogram. These methods are not parameterized
with the a priori knowledge about number of clusters or their shape. The
result depends on the discretization
resolution of the feature space and the quality of provided segmentations
can degrade if scale variations are present in the input
data. To overcome this drawback, the Hill-manipulation algorithm
[19] adaptively refines histogram resolution in the regions of
higher density.
Beside abovementioned, methods exist that combine information contained in
different attributes or apply postprocessing to improve the quality of the
segmentation acquired by color quantization [20,21].
Damir Krstinic
2011-11-04