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inefficient and computationally expensive, therefore we
use the following method to calculate the critical region in
section B.
III. FEATURES EXTRACTION
In the basketball video, the basic feature consists of
the color feature and histogram, which is sufficient for
cluster the frame into different classes. In this section the
color feature and histogram will be extracted as the input
of the classifier.
A. The court color detection
Based on the partition of the frames we propose the
implementation to detect the main color of the court. We
convert the RGB to HSL in region 2 defined in section 2
and calculate the main color as the frame’s color feature.
Due to the sensitivity of the human eye than the right
of the Hue and Saturation, we chose the Hue value of the
basketball court as the feature and calculate the proportion
of the court color.
B. The histogram of the R2 region
This section explains the histogram of the middlemost
region. Due to its usage of the statistic of the pixel’s light
and color without regard to the location of the pixel,
histogram is one of the most used methods to calculate the
inter-frame difference. Suppose D is the difference of two
frames, and D is defined as:
k
∑ − 1
k = 0
D = | H[
f ( x,
y,
t),
k]
− H[
f ( x,
y,
t + 1), k] | (1)
If the difference D is bigger than a threshold, the
conversion occurred in the lens.
B. The histogram of the key area
We propose a compute-easy, yet very efficient,
cinematographic algorithm for the frames with a high
value. We define regions by using Golden Section spatial
composition rule [8,9] , which suggests dividing up the
screen in 3 : 5 : 3 proportion in both directions, and
positioning the main subjects on the intersection points of
these lines. We have revised this rule for basketball video,
and divide the whole frame. In Figure 2, the examples of
the regions obtained by Golden Section rule is displayed
on several close up and in play shot.
IV. CLUSTERING BY C-MEANS
Clustering is a mathematical tool that attempts to
discover structures or certain patterns in a data set, where
the objects inside each cluster show a certain degree of
similarity. In the framework of fuzzy clustering, it allows
each feature vector to belong to more than one cluster with
different membership degrees (between 0 and 1) and
vague or fuzzy boundaries between clusters [10] . And also
an alternative way of NERF C-means is proposed recently
for any relational data clustering [11] .
Automatic recognition of frames is a challenging
problem and is highly demanded in an intelligent video
surveillance system. A video can be characterized by a
sequence of shots, each shot contains a frame sequence
which is difference from the others.
Our observation on Fig.2 has shown that similar
frames share a set of similar histogram. The process
involves feature extraction at the histogram to form a
descriptor for each frame and clustering of the frames into
a set.
A. Pros and cons of FCM
The popularity and usefulness of fuzzy C-means result
from three facts. The algorithms are simple; they are very
effective at finding minimizes of objective function J m
:
give data set
{ }
X = x1, x2, L xn
(2)
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