【摘 要】
：
Sharing traveling experience and photos on Social Network Service or Web albums is more and more popular recently.Good sightseeing photos in specific situation such as weather or season can impress to
【机 构】
：
Tokyo Metropolitan University,Japan
【出 处】
：
第三届先进计算智能与智能信息学国际研讨会(The Third International Workshop on Adv
论文部分内容阅读
Sharing traveling experience and photos on Social Network Service or Web albums is more and more popular recently.Good sightseeing photos in specific situation such as weather or season can impress tourists well,and be clues for them to consider where and when to visit for sightseeing.Regarding situations to be identified,this paper focuses on season.Compared with situations relating with weather and time of day(e.g.sunrise/sunset),whether or not different seasons have different sceneries depends on sightseeing spots.Therefore,classifying sightseeing spots into season-dependent/independent is required as preprocessing for season classification of sightseeing photos.This paper proposes a hybrid approach of machine learning with statistical features of sightseeing photos obtained from metadata in the first phase and color-based classification in the second phase.The experimental result of the first phase is shown.
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