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learning the weight coefficient of each feature by
training dataset and the classifiers constructed,
finally combining each SVM multi-class classifier
based on individual feature and the corresponding
weight coefficient into a complexity classifier.
Since our goal is to verify the efficiency of
image categorization algorithm we proposed, the
feature extraction method is simple and
straightforward, where color histogram (
Panchanathan,
Park, etc. 2000
), texture co-occurrence (Haralick,
Shanmugam, etc. 1973
) and shape invariant moment
(
Yao and Zhang, 2000
) are extracted for each image.
In the experiment, we use binary classifier in
LibSVM tool kits as core classifier to construct
SVM multi-class classifier (
Chang and Lin). Three
criterions are considered: precision, Recall, F-score.
Furthermore, in order to more clearly compare the
two methods, we compute macro-precision, macro-
recall, macro-F-score for each method.
Table 1: Results of general method.
Cat. B So V T
Precision 87.8% 93.5% 83.3% 100%
Recall 86% 86% 70% 88%
F-score 86.9% 89.6% 76.1% 93.6%
Cat. Sw Wl G Sh
Precision 95.7% 71.2% 81.3% 81.0%
Recall 90% 94% 78% 94%
F-score 92.8% 81.0% 79.6% 87.0%
Table 2: Results of our method.
Cat. B So V T
Precision 97.6% 93.3% 80.7% 100%
Recall 84% 84% 84% 86%
F-score 90.3% 88.4% 82.3% 92.5%
Cat. Sw Wl G Sh
Precision 90.3% 79.0% 80% 79.6%
Recall 94% 98% 80% 86%
F-score 92.1% 87.5% 80% 82.7%
Table 3: Comparison of the two methods.
Items Macro-
Precision
Macro-
Recall
Macro-F-
score
General
method
86.73% 86% 86.2%
Our
method
87.56% 87% 87.3%
As demonstrated in table 1 and table 2, our
method improves the performance of image
categorization comparing with general method. And
this improvement is quantitatively demonstrated by
their macro-precision, macro-recall and macro-F-
score comparison in table 3. Furthermore, the
method proposed in this paper has an obvious
advantage of automatically learning each feature’s
feature-weight, so that it has certain adaptive
capacity and adjusted ability, when it trains and
predicts other categories of images.
5 CONCLUSIONS
In this paper, an image categorization algorithm is
proposed to address the shorting of combining all
the features into one feature vector. The algorithm
firstly constructs SVM classifiers based on
individual feature and automatically learns each
feature’s weight coefficient, then combines SVM
classifiers and corresponding weight coefficient into
a complexity classifier. As demonstrated in the
experiments, our method improves the performance
of image categorization.
ACKNOWLEDGEMENTS
This work is supported by the national natural
science foundation of China (No. 60736044) and the
National High-Tech Development 863 Program of
China (No.2006AA010108).
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STUDY ON IMAGE CLASSIFICATION BASED ON SVM AND THE FUSION OF MULTIPLE FEATURES
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