![](bg4.png)
shows the image retrieval result that holds the ‘clear’
emotion in the center and the ‘street-fashion’
emotion in the circumference.
Figure 5: Retrieved results using Figure 2.
Figure 6: Retrieved results using two emotions ‘clear’ and
‘street-fashion’.
Figure 7: Retrieved results on the Corel database.
This paper has assessed the retrieval results
according to the precision and the number of
retrieved images. Precision is defined as the ratio of
the properly retrieved images to all the retrieved
images, and it is used to evaluate the ability of a
system to retrieve only proper images. The number
of retrieved images refers to the number of images
that are retrieved from the Corel database.
Figure 7 shows the average that has queries of 20
emotion words from the Corel database. The
experiment has been made for global emotion
retrieval (GE), local emotion retrieval by using only
one sub-area (LE1), and local emotion retrieval by
using two sub-areas (LE2). Figure 7 shows that the
retrieval result of using the proposed emotion sketch
gives better precision than the global approach.
5 CONCLUSIONS
This paper has proposed the image retrieval system
of using emotion sketch. The proposed retrieval
system as a content-based image retrieval system
includes the following technologies:
● Automatically extracting emotions from
images by using emotion colors and emotion
color words.
● Allowing the user to easily select the emotions
of each sub-area by providing convenient GUI
as is shown in Figure 4.
● Enabling the comparison of the emotional
similarities between images by suggesting a
method that can quantitatively calculate the
similarity of emotion words.
Also as compared to the results of existing emotion-
based image retrieval studies, this paper has
proposed a method that can find various emotions
that are scattered on the image rather than finding
one global emotion from one image.
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QUERY-BY-EMOTION SKETCH FOR LOCAL EMOTION-BASED IMAGE RETRIEVAL
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