Image Processing Reference
In-Depth Information
(4)
where
tf
i
represents the frequency of LCi;
i
; val
1
and val
2
express the added double weights on
the news title and the first paragraph, respectively.
4 Evaluation
In this experiment, we collected 18,000 images-resided web pages from Taiwan news website
udn.com
including the categories of politics, society, local news, world news, finance, and life
as the data sets. To verify the effectiveness of our proposed CLCP method, we used the im-
age caption as the ground truth label for the primary annotation to understand the degree to
which it matches the caption. We also invited subject experts and users to assess the appropri-
ateness of the secondary annotations. In the end, we measured the performance of the CLCP
method in a real-time mode.
4.1 Evaluation of Primary Annotation
Due to the fact of discrepancy in interpreting the context semantics by humans, therefore the
exact number of correct annotations of an image may not be easily identified. For example,
the LC “[
]” (President Ma Ying-jiu) is beter than its substrings “[
]” and “[ ]” even though these two substrings are also valid annotations. Thus, the
recall measurement did not apply to this study. A precision measurement was used to under-
stand the proportion of primary annotations actually matched the image captions as
(5)
.
(5)
where PAs represent the primary annotations from 18,000 documents. After the CLCP pro-
cessing, the total number of matched PAs in captions are 15,228. We obtained a precision rate
of 84.6%.
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