Discover "Anaba" Sightseeing Spots Using Social Images
スポンサーリンク
概要
- 論文の詳細を見る
Discovering diverse sightseeing resources is addressed more attentions to meet the increasing demand from personalized tour. "Anaba" spot is one of them which is less well-known but still worth visiting. In this paper, we pro-pose a novel method of using social geo-tagged images to discover "Anaba" spots. We first select possible candidates according to the visiting frequency asymmetry of photographers. Then, we evaluate the sightseeing score of each candidate by considering both social support and content quality of images shot around there. We will demonstrate the effectiveness of proposed approach on a collection of 3293 Flickr images.
- 一般社団法人電子情報通信学会の論文
- 2013-07-15
著者
-
Yoshikawa Masatoshi
Graduate School Of Informatics Kyoto University
-
Ma Qiang
Graduate School Of Informatics Kyoto University
-
ZHUANG CHENYI
Graduate School of Informatics Kyoto University
-
LIANG XUEFENG
Graduate School of Informatics Kyoto University
関連論文
- Processing XML Queries using rUID in SKEYRUS (データベースシステム研究報告 夏のデータベースワークショップ DBWS2002)
- Processing XML Queries using rUID in SKEYRUS
- Full-Text and Structural Indexing of XML Documents on B^+-Tree(Contents Technology and Web Information Systems)
- XSemantic : An Extension of LCA Based XML Semantic Search
- Design Framework of a Database for Structured Documents with Object Links (Special Issue on New Generation Database Technologies)
- An Efficient Schema-Based Technique for Querying XML Data(Database)
- News Bias Analysis Based on Stakeholder Mining
- How can the Web help Wikipedia? A Study of Information Complementation of Wikipedia by the Web
- Re-ranking Content Based Social Image Search Results by Multi Modal Relevance Feedback
- Incremental Construction of Causal Network from News Articles
- Incremental Construction of Causal Network from News Articles
- Mining and Explaining Relationships in Wikipedia
- Mining Knowledge on Relationships between Objects from the Web
- Discover "Anaba" Sightseeing Spots Using Social Images