IEEE Systems, Man and Cybernetics Magazine - April 2020 - 36

selection in image clustering. The proposed approach adaptively uses VAT with consideration of the built graph (VAT-G)
to draw an image G composed of vertices V and edges
E. The VAT-G model was used to choose the nearest and
farthest neighbors for performing clustering to satisfy both
within-cluster and between-cluster scatter criteria.
Synthetic aperture radar (SAR) provides a day or night,
all-weather means of remote sensing, which provides useful information about Earth. Spaceborne platforms continuously deliver enormous amounts of SAR data as
highly complicated images, which are almost impossible
to interpret manually. For automatic interpretation of SAR
images, Liu et al. [104] proposed an unsupervised classification framework that estimates the number of classes
(clusters) in an image using the VAT algorithm and the
DBE method [54].
An extension of this article by the same authors for a
special type of SAR image called polarimetric SAR (PolSAR) was presented in [105]. Their approach first partitioned the PolSAR image into superpixels, which are local,
coherent regions that preserve most of the characteristics
necessary for image information extraction, before using
the VAT and DBE algorithm for clustering and classification operations. Zou et  al. [106] proposed an alternate
approach to cluster superpixels based on mean Freeman
decomposition and hyperspectral-image color feature vectors using the VATdt approach [57], [58] to adaptively estimate the number of terrain classes and automatically
capture the cluster structure.
Wang et al. [19] present some interesting results related to the segmentation of digital images using their method of E-SpecVAT. Figure 16 is part of a set of outputs
made by the E-SpecVAT algorithm (see Figure 7 in [19]).
The input data set is a digital image from the Berkeley
image segmentation database, shown in Figure 16(a). The
size of the image is 431 # 321 = 154,301 pixels, so the dissimilarity matrix on pairs of pixels has a bit more than
N = 23 # 10 9 elements. There are k = 3 clearly visible
clusters in the input image corresponding to the steps
and entrance (darkest); the church (medium dark), and
the sky (lightest).
Figure 16(b) presents an E-SpecVAT view of the dissimilarity matrix made from a tiny sample of 300 pixels-that is,
approximately 0.2% of the input data-and it clearly shows
k = 3 clusters, with block pixel sizes roughly proportional to

(a)

(b)

(c)

Figure 16. An E-SpecVAT image analysis: (a) the

input image, (b) a 300 × 300 E-SpecVAT image, and
(c) the image segmentation.
36	

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Apri l 2020

the sizes of the three areas in the input image. Figure 16(c) is
the E-SpecVAT segmentation of the image using intensity as
the input feature (i.e., d j,k = ; I j - I k ;). This input image is
simple, so its segmentation was particularly challenging, but
this example shows the power of visualization with a VAT
model in the area of image processing.
Video Data Analysis
Discovering actionable knowledge from large volumes of
video data are of increasing interest to researchers. Examples of patterns that can be discovered from raw video
sequences include determining typical and anomalous patterns of activity, classifying activities into known categories (e.g., walking or riding), and discovering unknown
action patterns by clustering. Wang et al. [107] described a
tensor space representation for analyzing human activity
patterns in monocular videos and used the VAT algorithm
for the task of activity discovery from video data.
Application to Organizational Data
Enterprise software systems are large and complex. Security in such systems is of extreme importance for organizations. Role-based access control (RBAC) is an efficient
and flexible model for controlling computer resource
access and enforcing organizational policies. Deployment
and maintenance of RBAC requires role engineering,
which defines the set of roles that accurately reflects the
needs of the enterprise. Zhang et al. [108] proposed a VATbased role-engineering tool for the visual assessment of
user and permission tendencies (RoleVat) that produces
natural groupings of users and permissions and helps
determine the role permissions for different individuals
within the organization.
Another important challenge for deploying a software
system at a large enterprise is ensuring its smooth integration with many other interconnected systems, such as
mainframes, directory servers, databases, and other types
of software services. To do so, it is necessary to test it in
as realistic an environment as possible, before actual
deployment. However, getting access to the actual production environment for testing is usually impossible due to
the risk of disruption.
To address this problem, Du et  al. [109] and Versteeg
et al. [110] developed a VAT-based practical, scalable, and
fully automated approach to service emulation that uses
no explicit knowledge of the services, their message protocols, or structures and yet can simulate-to a high degree
of accuracy and scale-a realistic enterprise system
deployment environment. In both of these papers, VAT was
used to group the transactions by operation type, without
assuming any knowledge of the message format, which
was later used to generate the simulator output.
Application to Biomedical Engineering
The VAT family of algorithms has been extensively used
for understanding the data generated from biomedical



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