2016 Fiscal Year Research-status Report
Participatory Sensing and Felicitous Recommending of Venues
| Project/Area Number |
16K16058
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| Research Institution | National Institute of Informatics |
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Principal Investigator |
ュ イ 国立情報学研究所, コンテンツ科学研究系, 特任助教 (00754681)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | マルチモーダル分析 / 情報融合 / イベント検出 / パーソナライズド推薦 |
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| Outline of Annual Research Achievements |
Ｗe presented the EventSensor system that aims to address sentics understanding and produces a multimedia summary for a given mood. It extracts concepts and mood tags from visual content and textual metadata of UGCs, and exploits them in supporting several significant multimedia-related services such as a musical multimedia summary. This work has been published in Journal of Knowledge-Based Systems.
We explored the fusion of multimodal information to refine tag ranking leveraging recall based weighting. This work has been published in ACM Multimedia COMMONS Workshop.
We proposed a tag recommendation system, called, PROMPT, that recommends personalized tags for a given photo leveraging personal and social contexts. This work has been published in IEEE International Symposium on Multimedia.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The current research progress is going well. As I stated, we have published three works. We are working more tasks which are related to venue-related photo recognition and recommendation.
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| Strategy for Future Research Activity |
We plan to investigate two related works: (i) introducing diversity in multimedia summaries by leveraging visual concepts in photos and (ii) enabling users to obtain a multimedia summary of any event and mood. Relevance and diversity are the two main characteristics of a good multimedia summary. We plan to address the diversity criterion in our systems by performing the clustering of photos during pre-processing. Clusters are formed based on concepts derived from the content of photos and helpful in producing diverse multimedia summaries. For instance, clustering based on visual concepts helps in producing a multimedia summary with visually dissimilar photos ( i.e. , from different clusters).
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