Waseda meisei at TRECVID 2017: Ad-hoc video search

Kazuya Ueki*, Koji Hirakawa, Kotaro Kikuchi, Tetsuji Ogawa, Tetsunori Kobayashi


研究成果: Paper査読

7 被引用数 (Scopus)


The Waseda Meisei team participated in the TRECVID 2017 Ad-hoc Video Search (AVS) task [1]. For this year’s AVS task, we submitted both manually assisted and fully automatic runs. Our approach used the following processing steps: building a large semantic concept bank using pre-trained convolutional neural networks (CNNs) and support vector machines (SVMs), calculating each concept score for all test videos (IACC 3), manually or automatically extracting several search keywords based on the given query phrases, and combining the semantic concept scores to obtain the final search result. Our best manually assisted run achieved a mean average precision (mAP) of 21.6%, which ranked the highest among all the submitted runs. Our best fully automatic run achieved a mAP of 15.9%, which ranked second among all participants.

出版ステータスPublished - 2017
イベント2017 TREC Video Retrieval Evaluation, TRECVID 2017 - Gaithersburg, United States
継続期間: 2017 11月 132017 11月 15


Conference2017 TREC Video Retrieval Evaluation, TRECVID 2017
国/地域United States

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 情報システム
  • 信号処理


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