Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
Partial Denoising Boundary Image Matching Based on Time-Series Data |
ÀúÀÚ(Author) |
±è¹ü¼ö
ÀÌ»óÈÆ
¹®¾ç¼¼
Bum-Soo Kim
Sanghoon Lee
Yang-Sae Moon
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¿ø¹®¼ö·Ïó(Citation) |
VOL 41 NO. 11 PP. 0943 ~ 0957 (2014. 11) |
Çѱ۳»¿ë (Korean Abstract) |
À±°û¼± À̹ÌÁö ¸ÅĪ¿¡¼ À̹ÌÁöÀÇ ³ëÀÌÁ Á¦°ÅÇÏ´Â °ÍÀº Á÷°üÀûÀÌ°í Á¤È®ÇÑ ¸ÅĪÀ» À§ÇØ ¸Å
¿ì Áß¿äÇÑ ¿ä¼ÒÀÌ´Ù. º» ³í¹®¿¡¼´Â À±°û¼± À̹ÌÁö ¸ÅĪ¿¡¼ ºÎºÐ ³ëÀÌÁ Çã¿ëÇÏ´Â ¹®Á¦¸¦ ½Ã°è¿ µµ¸ÞÀο¡¼ ´Ù·é´Ù. À̸¦ À§ÇØ, ¸ÕÀú ºÎºÐ ³ëÀÌÁî Á¦°Å ½Ã°è¿(partial denoising time-series)À» Á¤ÀÇÇÏ¿© À̹ÌÁö µµ¸ÞÀÎÀÌ ¾Æ´Ñ ½Ã°è¿ µµ¸ÞÀο¡¼ ¸ÅĪ ¹®Á¦¸¦ ½Å¼ÓÇÏ°Ô ÇØ°áÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. ´ÙÀ½À¸·Î, µÎ À±°û¼± À̹ÌÁö, Áï ÁúÀÇ ½Ã°è¿°ú µ¥ÀÌÅÍ ½Ã°è¿¿¡¼ ±¸¼ºµÈ ºÎºÐ ³ëÀÌÁî Á¦°Å ½Ã°è¿µé °£¿¡ °¡Áú ¼ö ÀÖ´Â ÃּҰŸ®ÀÎ ºÎºÐ ³ëÀÌÁî Á¦°Å °Å¸®(partial denoising distance)¸¦ Á¦½ÃÇÑ´Ù. º» ³í¹®¿¡¼´Â À̸¦ µÎ À±°û¼± À̹ÌÁö °£ÀÇ À¯»ç¼º ôµµ·Î »ç¿ëÇÏ¿© À±°û¼± À̹ÌÁö ¸ÅĪÀ» ¼öÇàÇÑ´Ù. ±×·¯³ª, ºÎºÐ ³ëÀÌÁî Á¦°Å °Å¸®¸¦ ÃøÁ¤Çϱâ À§Çؼ´Â ¸Å¿ì ¸¹Àº °è»êÀÌ ºó¹øÇÏ°Ô ¹ß»ýÇϹǷÎ, º» ³í¹®¿¡¼´Â ºÎºÐ ³ëÀÌÁî Á¦°Å °Å¸®ÀÇ ÇÏÇÑÀ» ±¸ÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. ¸¶Áö¸·À¸·Î, ºÎºÐ ³ëÀÌÁî Á¦°Å À±°û¼± À̹ÌÁö ¸ÅĪÀÇ ÁúÀÇ ¹æ½Ä¿¡ µû¶ó ¹üÀ§ ÁúÀÇ ¸ÅĪ°ú k-NN ÁúÀÇ ¸ÅĪÀ» °¢°¢ Á¦¾ÈÇÑ´Ù. ½ÇÇè °á°ú, Á¦¾ÈÇÑ ºÎºÐ ³ëÀÌÁî Á¦°Å À±°û¼± À̹ÌÁö ¸ÅĪÀº ¼º´ÉÀ» ¼ö ¹è¿¡¼ ¼ö½Ê ¹è±îÁö Çâ»ó½ÃŲ °ÍÀ¸·Î ³ªÅ¸³µ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Removing noise, called denoising, is an essential factor for the more intuitive and more accurate results in boundary image matching. This paper deals with a partial denoising problem that tries to allow a limited amount of partial noise embedded in boundary images. To solve this problem, we first define partial denoising time-series which can be generated from an original image time-series by removing a variety of partial noises and propose an efficient mechanism that quickly obtains those partial denoising time-series in the time-series domain rather than the image domain. We next present the partial denoising distance, which is the minimum distance from a query time-series to all possible partial denoising time-series generated from a data time-series, and we use this partial denoising distance as a similarity measure in boundary image matching. Using the partial denoising distance, however, incurs a severe computational overhead since there are a large number of partial denoising time-series to be considered. To solve this problem, we derive a tight lower bound for the partial denoising distance and formally prove its correctness. We also propose range and k-NN search algorithms exploiting the partial denoising distance in boundary image matching. Through extensive experiments, we finally show that our lower bound-based approach improves search performance by up to an order of magnitude in partial denoising-based boundary image matching. |
Å°¿öµå(Keyword) |
Time-series databases
boundary image matching
time-series matching
partial denoising
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