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The vast amount of online available images requires a
certain amount of descriptive tags to images, because generic online
image sharing services are using tags to deal with searching, managing
and organizing images. To do that, users have possibility to register
appropriate tags to images when uploading these images. Image searching
engines can then retrieve images by relying on tag-based search
techniques. However, those tags annotated by users are not always
representing the actual image. They are likely to be highly subjective.
Sometimes, some tags are not related to the visual perception of the image. To overcome the limitation, this
research proposes a method to measure tag confidence for images in
database so that one can differentiate confidence tags from noisy tags.
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Proposed Method
Overall Procedure
We define tag confidence as how much the tag is related to
associate the image. Tag confidence value is defined varying from 0 to 1.
If the tag is not related to the image, tag confidence value is close to
0. Likewise, as tag confidence value is close to 1, the tag is highly
related to the image.
Fig.
1 illustrates the overall procedure of the proposed tag confidence
measurement. The tag confidence is calculated based on the visual
information of image. Let us assume the image database in Fig. 1 consist
of images from UGC and associated tags. We suppose to measure tag
confidence for all tags with associated images in the database. To do
that, a target image is selected from the image database and the tag
confidence for the target image is measured. All images in the database
are selected as target images so that confidence values of all tags in
the database are measured.
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Fig. 1. Overall procedure of proposed
tag confidence measurement
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Semantic Vector
Generation
A semantic vector aggregates the confidence values that
are the result of applying a series of independent classifiers to an
image. Each confidence value represents the degree of certainty in which
a particular concept has been detected. Each element of a semantic
vector, i.e. the confidence value for a particular concept, is generated
from a collection of training images that all contain the same concept.
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Likelihood Image
Selection
Likelihood images in the database, which are visually
similar to the target image, are obtained with the semantic vectors
mentioned above. The distance between two vectors of the target image and
an image in the database is calculated with L2 distance. Since
each element of semantic vector represents the probability of the
semantic concepts, the maximum distance is 1. The selected likelihood
images are I1, ¡¦, Itho which are represented in
order of similarity distance. Image in the database having small
similarity distance means it is close to the target image.
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Tag Confidence
Measurement
Each tag confidence in the target image is supposed to be
measured. Let nth tag of target image be Ttn. It is
calculated by analyzing the word similarity of tags in likelihood images.
We adopt WordNet to calculate the similarity among tags. Fig. 2 shows the
schematic structure of the tag confidence measurement.
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Fig. 2. Proposed tag confidence
measure
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¡¡
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Sihyoung
Lee
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ijiat@kaist.ac.kr
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Ph.D Candidate in Dept. of Information and Communications Engineering in KAIST
Senior Researcher in IVY Lab in KAIST
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Experience of Research projects
Home photo categorization
Multimedia application format for e-learning
Automatic image annotation
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Research Interest |
Image Tag Recommendation, Social Media Processing
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¡¡ |
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Hyun
Seok Min
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hsmin@kaist.ac.kr
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Ph.D Candidate in Dept. of Information and Communications Engineering in KAIST
Senior Researcher in IVY Lab in KAIST
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Experience of Research projects |
Use of Semantic Features for Filtering of Malicious Content in an IPTV Environment
Enhancement of Face Detection Using Spatial Context Information
Semantic Annotation of Personal Video Content Using an Image Folksonomy
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Research Interest |
Video annotation, Video copy detection, Face detection, Video event detection
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¡¡ |
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À̽ÃÇü, ¹ÎÇö¼®, ÀÌ¿µº¹, ³ë¿ë¸¸, ¡°UCC ¿µ»óÀÇ »ç¿ëÀÚ ÅÂ±× ½Å·Ú¼º ÃøÁ¤¿¡ °üÇÑ¿¬±¸,¡± Çѱ¹½Åȣ󸮽ýºÅÛÇÐȸ
ÇÏ°èÇмú´ëȸ
Á¦9±Ç1È£, pp.68~71, °æ³²´ëÇб³, June 28, 2008 (selected
as An Excellent Paper Award) |
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Sihyoung Lee, Hyun-Seok Min, Young
Bok Lee, Yong Man Ro, ¡°Measurement of Tag Confidence in User Generated
Contents Retrieval,¡± SPIE 2009, Jan.18~22, 2009, San Jose, USA (accepted) |
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