Video semantic indexing using image classification

Ming Yang, Yuanqing Lin, Fengjun Lv, Shenghuo Zhu, Kai Yu, Mert Dikmen, Liangliang Cao, Thomas S. Huang

Research output: Contribution to conferencePaper

Abstract

This notebook paper summarizes Team NEC-UIUC's approaches for TRECVid 2010 Evaluation of Semantic Indexing. Our submissions mainly take advantage of advanced image classification methods using linear coordinate coding (LCC) of local features powered by the distributed computing software Hadoop. For every video shot, we evenly sample key frames and extract dense local features including DHOG and LBP, which are encoded by linear coordinate coding. Then, for every concept large-scale linear SVM classifiers are trained based on spatial pyramid of LCC features. Finally, we employ multiple instance learning to rank the video shots according to the SVM scores of individual frames. Our systems achieve mean extended inferred average precision (mean xinfAP) 7.40% for the 30 concepts evaluated by NIST and mean average precision 28.63% using 1/5 of the development data as the validation set for the total 130 concepts.

Original languageEnglish (US)
StatePublished - Jan 1 2010
EventTREC Video Retrieval Evaluation, TRECVID 2010 - Gaithersburg, MD, United States
Duration: Nov 15 2010Nov 17 2010

Other

OtherTREC Video Retrieval Evaluation, TRECVID 2010
CountryUnited States
CityGaithersburg, MD
Period11/15/1011/17/10

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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  • Cite this

    Yang, M., Lin, Y., Lv, F., Zhu, S., Yu, K., Dikmen, M., Cao, L., & Huang, T. S. (2010). Video semantic indexing using image classification. Paper presented at TREC Video Retrieval Evaluation, TRECVID 2010, Gaithersburg, MD, United States.