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esult is the probability value of each sample belonging to
the whole posture cluster samples, and the posture cluster
is decided by the maximum probability. In this paper
N is 3314 and cluster number M is 3. Some clustering
results are show in Fig.3 and Fig.4.
It intuitively satisfies human senses that sample frames
in Fig.4 are classified into the same cluster, because they
are indeed of similar histogram or main color.
V. EXPERIMENTAL RESULTS
In this section, we describe the experiments aimed at
evaluating the proposed method, which integrated into a
system that was tested using two basketball video
sequences.
A. Shots boundary detection
Our proposed shot boundary detection approach has
been tested on two matches of basketball from the 2009
All Star NBA matches. The first video is 20 minutes long
and contains 127 shots. The second video is of 30 minutes
and contains 233 shots. From table 1 the accuracy of our
shot boundary detection algorithm is about 93.7%. The
false detection may be caused by photo flash, for instance,
if the difference of the histogram of the frames located at
the photo flash will be tremendous, consequently, will
result in incorrect detection. Table 1 lists the performance
in terms of the precision and recall.
The dissimilarity matching on two frames are all
based on the histogram of the frames shown in Fig.2. A
promising performance, Recall 85%-93%, and precision
90%-95%, has been achieved.
TABLE1.
THE RSULTOF OUR SHOT BOUNDARY
DETECTION
manually auto precision recall
Video 1 127 106 96.4% 91%
Video 2 233 212 98% 96%
C. Shot classification in basketball video
The total length of the basketball video is about 60
minutes (153 shots) consisting of NBA All Star match (30
min) and CBA (30 min). Table 2 shows recall and
precision of shot classification. The lower accuracy of the
NBA sequence is caused by close up of players having
skin colors similar to play court.
TABLE 2. THE RESULT OF OUR SHOT CLASSIFICATION
M: Manually A: auto R: recall P: precision
In-play shot
Non-play shot
M A R P M A R P
VI. CONCLUSIONS
We have presented an effective high-level semantic
concept of “semantic shot classes”, which frequently
occurs in broadcast sports video, and compared four fuzzy
c-means algorithms and found serious shortcomings for
fuzzy c-means. In order to detect this concept, we have
proposed a method for semantic shot classification,
consequently we give two improved versions for FRCM,
and we carried out our research on image clustering with
NERFCM effectively. Experiments have shown that an
appropriate construction of mid-level representations can
improve the accuracy and flexibility of shot classification.
We have justified the proposed mid-level
representations through the task of video shot
classification. Our future work includes the evaluation of
individual features for various tasks; extension of the
proposed framework to different sports, such as football,
basketball, and baseball, which require different event and
object detection modules.
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NBA 16 18 100% 88.9% 69 65 92.8% 98.5%
CBA 21 20 90.5% 95% 83 77 90.4% 97.4%
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