Signal Analysis Research (SAR) Group - RNet - Ryerson University

TABLE I
EXPERIMENTAL RESULTS IN TERMS OF RR FOR THE CBIR SYSTEM WITH
NO FEATURE WEIGHT DETECTION MECHANISM FIG 4)
Classifiera Set A Set B Set C Average
Ncut 41 3% 47 3% 51 9% 46.8%
SOFM 51 1% 44 3% 56 6% 50.7%
SOTM 51 5% 51 1% 58 4% 53.7%
SONcut 51.9% 52.8% 57.0% 53.9%
TABLE 11
EXPERIMENTAL RESULTS IN TERMS OF RR FOR THE CBIR SYSTEM WITH
GA-BASED FEATURE WEIGHT DETECTION ALGORITHM FIG 5) [7]
~lasslfier~ Set A Set B Set C Average
Ncut 67 3% 64 5% 65 1% 61 6%
SOFM 65 1% 66 7% 68 3% 66.7%
SOTM 66 8% 72 1% 74 4% 71.1%
SONcut 68 8% 72 8% 73 9% 71.8%
DSOTM 78 3% 76 7% 80 5% 78.5%
a Ncut Normalized Graph Cuts, SONcut Self-Organizing Normalized
Graph Cuts, SOFM Self-Organizing Feature Maps, SOTM Self-
Organizing Tree Maps, DSOTM Directed Self-Organizing Tree Maps
colour images, covering a wide range of real-life photos,
from 51 different categories. Each category consisted of 100
visually associated objects to simplify measurements of the
retrieval accuracy (RR) during the experiments. 3 sets of 5 1
images were drawn from the database to form sets A, B, and
C. Each set consists of randomly selected images such that
no two images were from the same class. Retrieval results
were statistically calculated ti-om each of the 3 sets. In the
simulations, a total of 16 most relevant images were
retrieved to evaluate precision of the retrieval.
The experiment results are illustrated in Table 1. In the
Ncut, SOFM, and SOTM algorithms, the maximum number
of allowed cluster generation was set to P, P < Q, where Q
is the total number of retrieved images from the initial
search; P was empirically set to 8. A 4x2 grid topology was
used in the SOFM structure to locate maximum 8 possible
cluster centres. A hard decision on the resemblance of the
input samples was made: if the sample is closer to one
centre than any other centres, in terms of a predefined
distance metric, it belongs to that centre. Table 2 illustrates
results aRer feature weight detection using GA-based
method described in [I.
Although the Ncut algorithm is a top-down classification
process and aims to extract global impressions of the input
pattern and present a hierarchical description of it,
employing a predictive mechanism to estimate true number
of clusters prior to spawning new neurons is proven to be
beneficial. This predictive mechanism will enforce afrontier
on the classification process and inhibits unnecessary
centres to be generated around the query position. As a
result, a more accurate impression of relevance can be
achieved by using SONcut algorithm.
The SOTM algorithm not only extracts global intuitions
of the input pattern, it also introduces some degree of
localization into the discriminative process to achieve
maximal discrimination at any given resolution (or number
of classes.) Moreover, the ability of SOTM to span and force
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division in the extremes of the data in the early, delaying
division of most similar aspects until later stages of learning,
and a flexible tree-like topologies (more plastic than SOFM)
makes it essentially sensitive to the most dominant
differences in the data and, thus, less prone to classification
errors and more attractive to the retrieval applications.
Despite all the above advantages of using SOW-based
classifiers, retaining some degree of supervision to prevent
unnecessary boundaries from forming around the query
class seems to be crucial. The DSOTM algorithm not only
provides a partial supervision on cluster generation by
forcing divisions away ti-om the query class, it also makes a
soft decision on resemblance of the input patterns by
constantly modifying each sample's membership during
learning phase of the algorithm. As a result, a more robust
tree structure as well as a better sense of likeness can be
finally achieved.
V. CONCLUSION
The framework for a novel unsupervised clustering
algorithm based on DSOTM was introduced in this work. A
modification on the current structure of Ncut algorithm was
also proposed in this paper. This modification provides a-
priori knowledge for the algorithm to determine appropriate
number of clusters, based on principles found in DSOTM,
prior to its hierarchical clustering operation. Performance of
the proposed methods was compared with other
conventional clustering methods (i.e Ncut, SOFM, and
SOTM) by using a computer controlled CBIR system.
SOTM outperforms both Ncut and SOFM and performs
fairly close to SONcut even with its blind top-down data
exploration. This is due to its flexible tree-shape structure as
well as its competitive learning algorithm that injects some
degree of localization into the discriminative process. The
experimental results also illustrate promising performance of
utilizing DSOTM in the structure of automatic CBIR
engines.
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