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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ³í¹®Áö C : ÄÄÇ»ÆÃÀÇ ½ÇÁ¦

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Current Result Document : 6 / 7 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) »çÁø ÄÜÅÙÃ÷ ºÐ·ù¸¦ À§ÇÑ ÅÂ±× Å¬·¯½ºÅ͸µ ±â¹ý ¹× ÅÂ±× Ãßõ
¿µ¹®Á¦¸ñ(English Title) A Tag Clustering and Recommendation Method for Photo Categorization
ÀúÀÚ(Author) ¿øÁöÇö   ÀÌÁ¾¿ì   ¹ÚÈñ¹Î   Ji-Hyeon Won   Jongwoo Lee   Heemin Park  
¿ø¹®¼ö·Ïó(Citation) VOL 14 NO. 02 PP. 0001 ~ 0013 (2013. 04)
Çѱ۳»¿ë
(Korean Abstract)
°³ÀÎÀÌ »ç¿ëÇÒ ¼ö ÀÖ´Â ½º¸¶Æ® ±â±â°¡ ´Ù¾çÇØÁö¸é¼­ ¿©·¯ ±â±â·Î »ý»êµÈ »çÁø ÄÜÅÙÃ÷°¡ ¾î¶² ±âÁØÀ̳ª ±ÔÄ¢ ¾øÀÌ ºÐ»êµÇ¾î ÀÖ¾î ÄÜÅÙÃ÷¸¦ °ü¸®ÇÏ°í ¿øÇÏ´Â ÄÜÅÙÃ÷¸¦ °Ë»öÇÏ´Â °ÍÀÌ ¾î·Á¿öÁ³´Ù. µû¶ó¼­ º» ³í¹®¿¡¼­´Â °³ÀÎ »çÁø ÄÜÅÙÃ÷¸¦ È¿°úÀûÀ¸·Î ºÐ·ùÇϱâ À§ÇÏ¿© ÀǹÌÀû À¯»çµµ¸¦ ±â¹ÝÀ¸·Î ÇÑ ÅÂ±× Å¬·¯½ºÅ͸µ ±â¹ý°ú °³ÀÎÀÌ »çÁø¿¡ ű׸¦ ³ÖÀ» ¶§ Ãʱâ Ŭ·¯½ºÅ͸¦ ±â¹ÝÀ¸·Î ű׸¦ ÃßõÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. ÅÂ±×µé »çÀÌÀÇ À¯»çµµ¸¦ °è»êÇÏ¿© ¼­·Î ¿¬°üÀÌ ÀÖ´Ù°í ÆǴܵǴ Å±׵éÀ» Ŭ·¯½ºÅ͸µ Çϴµ¥, űװ¡ °°Àº Ŭ·¯½ºÅÍ¿¡ Æ÷ÇԵǾî ÀÖÀ¸¸é ±× ű׸¦ °¡Áø »ç¶÷µéµµ À¯»ç¼ºÀ» °¡Áø´Ù°í º¼ ¼ö ÀÖÀ¸¹Ç·Î °³ÀÎ »çÁøµéÀ» Àǹ̿¡ µû¶ó ºÐ·ùÇϴµ¥ ÀÌ¿ëÇÒ ¼ö ÀÖ´Ù. ¶ÇÇÑ ºÐ·ùµÈ Ãʱâ Ŭ·¯½ºÅͷΠű׸¦ ÃßõÇÏ¿© °³ÀÎ »ç¿ëÀÚ°¡ ű׸¦ ºÐ·ù¿¡ ¸Â°Ô Ãß°¡ÇÒ ¼ö ÀÖ¾î »çÁø ºÐ·ù °ü¸®°¡ ¿ëÀÌÇØ Áø´Ù.
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(English Abstract)
Recent advance and popularization of smart devices and web application services based on cloud computing have made end-users to directly produce and, at the same time, consume the image contents. This leads to demands of unified contents management services. Thus, this paper proposestag clustering method based on semantic similarity for effective image categorization. We calculate the cost of semantic similarity between tags and cluster tags that are closely related. If tags are in a cluster, we suppose that images with them are also in a same cluster. Furthermore, we could recommend tags for new images on the basis of initial clusters.
Å°¿öµå(Keyword) ÅÂ±× ºÐ·ù   ÅÂ±× Ãßõ   Ŭ·¯½ºÅ͸µ   »çÁø ÄÜÅÙÃ÷   »çÁø °ü¸®   Tag Categorization   Tag Recommendation   Clustering   Photo Contents   Photo Management  
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