( 1) 3. Hilliges 1 Fig. 1 Overview image of the system 3) PhotoTOC 5) 1993 DigitalDesk 7) DigitalDesk Koike 2) Microsoft J.Kim 4). 2 c 2010
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1 1 2 2 Automatic Tagging System through Discussing Photos Kazuma Mishimagi, 1 Masashi Toda 2 and Toshio Kawashima 2 Many media forms can be stored easily at present. Photographs, for example, can be easily stored even though most of them have not been edited. This means they will gradually lose their value and become essentially unusable. To make better use of photographs, we tried to make use of information provided by viewers who had seen and commented on them. We felt that analyzing this information would enable us to make maximum use of photographic data. To do this, we defined a tag propagation model and relationships between photos. We also proposed a system that uses image processing to analyze viewers handling of photos and how the photos are relevant to each other. We then validated our model by using it. 1 Graduate School of Future University-Hakodate 2 Future University-Hakodate 1. () 1 c 2010 Information Processing Society of Japan
2 ( 1) 3. Hilliges 1 Fig. 1 Overview image of the system 3) PhotoTOC 5) 1993 DigitalDesk 7) DigitalDesk Koike 2) Microsoft J.Kim 4). 2 c 2010 Information Processing Society of Japan
3 flickr 8) flickr 6) flickr () Fig. 2 Position(Moving) 4 () Fig. 4 Rotation(angle) 3 Fig. 3 Overlap 5 Fig. 5 Pointed c 2010 Information Processing Society of Japan
4 P 1 I1 P 2 I2 I1 I2 ϕ (1) P 1 P I1 I2 ϕ P 1 P 2 I P 1 I T T P % 15% (0.5 ) 1 ( 7) HSV Hue 0-30 ( 5000px) 80px SURF SURF 1) 64 ID SURF 4 c 2010 Information Processing Society of Japan
5 Table 1 1 Patterns of tag propagation SURF ID SURF 8 PC ID SURF 30px 5 c 2010 Information Processing Society of Japan
6 Fig. 7 7 Detection of photos 2 Table 2 Definitions of weights 80px 1, , 0 1 1, 0 6 Fig. 6 Process overview px x480 30fps 120cm PC Core2 Duo 2.2 GHz CPU 2 GB RAM 6 c 2010 Information Processing Society of Japan
7 % PC Fig. 8 Photo on the table matched the data stored in PC A 40 A A A % 7 c 2010 Information Processing Society of Japan
8 7. Fig. 9 9 Example of retrieving results 1) Bay, H., Tuytelaars, T. and Gool, L. J.V.: SURF: Speeded Up Robust Features, ECCV (1) (Leonardis, A., Bischof, H. and Pinz, A., eds.), Lecture Notes in Computer Science, Vol.3951, Springer, pp (2006). 2) HidekiKoike, ShintaroKajiwara, K.F. and Sato, Y.: Information Layout and Interaction on Virtual and Real Rotary Tables, Second Annual IEEE International Workshop on Horizontal Interactive Human-Computer System, pp (2007). 3) Hilliges, O., Kunath, P. and Pryakhin, A.: Browsing and Sorting Digital Pictures Using Automatic Image Classification and Quality Analysis, 3, pp , Springer-Verlag (2007). 4) Kim, J., Seitz, S.M. and Agrawala, M.: Video-based document tracking: unifying your physical and electronic desktops, Proceedings of the 17th annual ACM symposium on User interface software and technology, Santa Fe, NM, USA, ACM, pp (2004). 5) Platt, J.C., Czerwinski, M. and Field, B.A.: PhotoTOC: Automatic Clustering for Browsing Personal Photographs, Technical report, Microsoft Research (2002). 6) Terrenghi, L., Terrenghi, L., Kirk, D., Sellen, A. and Izadi, S.: Affordances for manipulation of physical versus digital media on interactive surfaces, Proceedings of the SIGCHI conference on Human factors in computing systems, San Jose, California, USA, ACM, pp (2007). 7) Wellner, P.: Interacting with paper on the DigitalDesk, Communications of the ACM, Vol.35, No.7, pp (1993). 8) Yahoo!Inc.: Flickr, 8 c 2010 Information Processing Society of Japan
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