TY - GEN
T1 - Event clustering of lifelog image sequence using emotional and image similarity features
AU - Ratsamee, Photchara
AU - Mae, Yasushi
AU - Kojima, Masaru
AU - Horade, Mitsuhiro
AU - Kamiyama, Kazuto
AU - Arai, Tatsuo
PY - 2014
Y1 - 2014
N2 - Lifelog image clustering is the process of grouping images into events based on image similarities. Until now, groups of images with low variance can be easily clustered, but clustering images with high variance is still a problem. In this paper, we challenge the problem of high variance, and present a methodology to accurately cluster images into their corresponding events. We introduce a new approach based on rank-order distance techniques using a combination of image similarity and an emotional feature measured from a biosensor. We demonstrate that emotional features along with rank-order distance based clustering can be used to cluster groups of images with low, medium, and high variance. Experimental evidence suggests that compared to average clustering precision rate (65.2%) from approaches that only consider image visual features, our technique achieves a higher precision rate (85.5%) when emotional features are integrated.
AB - Lifelog image clustering is the process of grouping images into events based on image similarities. Until now, groups of images with low variance can be easily clustered, but clustering images with high variance is still a problem. In this paper, we challenge the problem of high variance, and present a methodology to accurately cluster images into their corresponding events. We introduce a new approach based on rank-order distance techniques using a combination of image similarity and an emotional feature measured from a biosensor. We demonstrate that emotional features along with rank-order distance based clustering can be used to cluster groups of images with low, medium, and high variance. Experimental evidence suggests that compared to average clustering precision rate (65.2%) from approaches that only consider image visual features, our technique achieves a higher precision rate (85.5%) when emotional features are integrated.
KW - Lifelog image clustering
KW - Rank-order distance based clustering and high variance event
UR - http://www.scopus.com/inward/record.url?scp=84906898986&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84906898986
SN - 9789897580031
T3 - VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
SP - 618
EP - 624
BT - VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
PB - SciTePress
T2 - 9th International Conference on Computer Vision Theory and Applications, VISAPP 2014
Y2 - 5 January 2014 through 8 January 2014
ER -