Da. Langan et al., CLUSTER VALIDATION FOR UNSUPERVISED STOCHASTIC MODEL-BASED IMAGE SEGMENTATION, IEEE transactions on image processing, 7(2), 1998, pp. 180-195
Citations number
39
Categorie Soggetti
Computer Science Software Graphycs Programming","Computer Science Theory & Methods","Engineering, Eletrical & Electronic","Computer Science Software Graphycs Programming","Computer Science Theory & Methods
Image segmentation is an important and early processing stage in many
image analysis problems. Often, this must be done in an unsupervised f
ashion in that training data is not available and the class-conditione
d feature vectors must be estimated directly from the data. A major pr
oblem in such applications is the determination of the number of class
es actually present in an image. This problem, called the cluster vali
dation problem, remains essentially unsolved. In this paper, we invest
igate the cluster validation problem associated with the use of a prev
iously developed unsupervised segmentation algorithm based upon the ex
pectation-maximization (EM) algorithm. More specifically, we consider
several well-known information-theoretic criteria (IC's) as candidate
solutions to the validation problem when used in conjunction with this
EM-based segmentation scheme, We show that these criteria generally p
rovide inappropriate solutions due to the domination of the penalty te
rm by the associated log-likelihood function, As an alternative we pro
pose a model-fitting technique in which the complete data log-likeliho
od functional is modeled as an exponential function in the number of c
lasses acting. The estimated number of classes are then determined in
a manner similar to finding the rise time of the exponential function.
This new validation technique is shown to be robust and outperform th
e IC's in our experiments. Experimental results for both synthetic and
real world imagery are detailed.