Anecdotes extraction from webpage context as image annotation - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 299

4.2 Expert Evaluation of Secondary Annotation
To evaluate the validity of the secondary annotations, we invited five subject experts to parti-
cipate in the assessment. Thirty pieces of news were randomly selected from which we extrac-
ted 60 secondary annotations. To reduce ambiguous judgements, each annotation was evalu-
ated based on a method of dichotomic classification to which the annotation represents the im-
age content. Each expert could only identify either “agree” or “disagree” for each annotation.
The result showed that the number of check marks of consent is 252 of 300. It implies that the
agreement of the appropriateness of the secondary annotations to the images achieves 84%.

4.3 User Evaluation of Secondary Annotation
To evaluate the appropriateness of the secondary annotation, we conducted another survey
to understand the differences between the image annotation and the users' expectation. Sixty
graduate and undergraduate students were recruited from National Yunlin University of
Science & Technology, Taiwan to participate in the assessment. One hundred pieces of news
were randomly selected from which we collected 200 secondary annotations. Students were
divided into four groups, and each group was given 25 images for the evaluation. To assist
the assessment, we provided news title, text and caption for references. Each annotation was
evaluated by three participants to understand the degree to which the annotations appropri-
ately address the image content. The result in Table 1 shows that the number of check marks
of agreement is much higher than that of disagreement. The agreement rate of user evaluation
reaches 76.6%.

Table 1
Results of User Agreement

Highly Agree Agree Average Disagree Highly Disagree Total

1380 920 423 204 73 3000

4.4 Results of Image Annotation
The following three examples display the results after conducting CLCP. For copyright con-
sideration, we refer the indicated image to its url address for reference. Table 2 is a local

news entitled “ ” (The automobile life-sav-
ing buoy, Europe science competition won the gold medal). The primary and secondary

annotations were generated, including “[ ]”
(Dr. Zhang Faxian, Department of Electronic Engineering at Cheng Shiu University), “[

]” (Combined Type of Rescue buoyancy Suite), and “[

]” (The automobile life-saving buoy). Note that our method extracts

“[ ],” the inventor of this awarded buoy, is
not mentioned in the news title and it is so specific and completely it the picture. Table 3 is an