NDLI: Personal photo organization using event annotation

Content Provider	IEEE Xplore Digital Library
Author	Jia-Min Gu Yi-Leh Wu Wei-Chih Hung Cheng-Yuan Tang
Copyright Year	2013
Description	Author affiliation: Dept. of Inf. Manage., Huafan Univ., Taipei, Taiwan (Cheng-Yuan Tang) \|\| Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan (Jia-Min Gu; Yi-Leh Wu; Wei-Chih Hung)
Abstract	Photo events in personal photo collection management are important because people take photos according to the specific events. Event is one of important cues to retrieve photos which are well remembered by people. However, event classification work needs some useful cues to annotate the photo events well. Our main contribution is to annotate photo events automatically using simple temporal cues such as month, weekday/weekends, day/night, the correlations between scene and events, and the relation between concepts such as people/non-people and indoor/outdoor and events. In this paper, we propose an automatic photo event classification framework to address these problems. The framework is composed of four main components: the temporal detector, the scene detector, the concept detector, and the event detector. Our experimental results show that the methods in the framework are useful to detect the events of personal photos. According to the contents of events, we now can detect four events: beachfun, graduation, urbantour, and yardprk, and are expanding to detect other events.
Starting Page	1
Ending Page	4
File Size	211055
Page Count	4
File Format	PDF
e-ISBN	9781479904341
DOI	10.1109/ICICS.2013.6782958
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-10
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Support vector machines Face Conferences Computer vision Cities and towns Buildings SVM Personal photo collection exif face detection event classification
Content Type	Text
Resource Type	Article

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