Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
Online ISSN : 1881-7203
Print ISSN : 1347-7986
ISSN-L : 1347-7986
Original Papers
Social Group Discovery Extracting Useful Features using Multiple Instance Learning
Ryota SATOHitoshi HABEIkuhisa MITSUGAMISatoru SATAKEKazuhiko SUMIYasushi YAGI
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2016 Volume 28 Issue 6 Pages 920-931


If we can detect groups of visitors in public spaces and commercial facilities, we can provide information depending on the attributes of the groups, and we can also provide statistics with regards to the usage of the facilities for the owners of the facilities. The features, such as person-to-person distance and gaze direction, is useful for group detection and have been used in a number of works. However, almost all of the works extract the features from the whole data. This causes miss-detection in some cases. Even when we are walking with a fiend or colleague, we do not interact with the others all of the time. This means that a meaningful information for group detection is embedded within a part of timeseries data, not all of the data. We have to pick up the meaningful information and ignore the others. To this end, we divide whole of the time-series data into a set of data along the time axis. We apply the multiple instance learning (MIL) to find out the meaningful information among the data. The features computed for each time slot are treated as instances in MIL. MIL can extract one or more positive instance from all of the instances, including positive and negative ones. We conducted experiments using two types of data: simulated group actions and actual actions. Our method outperforms the existing method for both of the data.

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© 2016 Japan Society for Fuzzy Theory and Intelligent Informatics
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