Computational Intelligence - February 2013 - 45
for the final determination. According to [4], these partition
values were selected because of their relatively good test
detection performance among different experimental trials. It
was shown in [4] that this non-uniform partition detection
method outperformed the detection method using color histogram features from the uniformly partitioned HS space and
the TFS-SVMPC classifier. The number of object and nonobject training patterns used to train the two TFS-SVMPCs
were the same as those used in the FC-SSCSVM. Table 4
shows the detection results. The proposed method shows
much better detection performance than the global-localcolor detection method in experiments 3, 4, and 5. The performance of the global-local-color detection method is competitive with the proposed method in the other two
experiments. Overall, the proposed method outperforms the
global-local-color detection method.
VI. Conclusions
This paper proposed a novel entropy-based FC-SSCSVM
detection method to detect objects in different complex
scenes. The SSC algorithm is recommended to find a good
clustering result for color histogram extraction and antecedent-parameter learning of the FC-SSCSVM. For the two
proposed entropy features, the single EEC value used in filtering phase one enables rapid filtering via comparison
against a threshold. The EGCD feature vector in filtering
phase two considered the geometric distributions of the clustered composing colors of an object and therefore reduced
the number of FAs and improved detection performance. For
FC-SSCSVM learning, the use of a linear SVM helped in
finding a set of consequent parameters with high generalization ability. The experimental results showed that the FCSSCSVM outperformed the neural and SVM-trained FCs
used for comparison. The comparison with different object
detection methods utilizing different detection features also
showed the advantage of the proposed method. In the future,
the proposed method will be applied to stereo, camera-based,
three-dimensional object localization problems.
VII. Acknowledgments
This work was supported by the National Science Council,
Taiwan, R.O.C. under Grant NSC101-2221-E-005036-MY2.
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