A system for TRecViD med by MCIS

Yang Feng; Wanchen Sui; Xinxiao Wu

A system for TRecViD med by MCIS

Yang Feng
, Wanchen Sui
, Xinxiao Wu

计算机学院

Beijing Institute of Technology

科研成果: 会议稿件 › 论文 › 同行评审

摘要

We designed a simple system for the 2014 TRECVID Multimedia Event Detection [1]. Except the videos provided by NIST, we only used the BVLC Reference CaffeNet model file distributed besides Caffe [2]. Our system follows the standard pipeline and consists two parts: feature extraction and classification. The feature extraction part is implemented by Caffe and the classification is implemented by LIBSVM [3]. Based on the results, we think that the contribution mainly comes from the feature extraction part. We learned that Convolutional Neural Networks (CNN) is a powerfully model and hope that a easy accessible spatio-temporal CNN model for videos will be available soon.

源语言	英语
出版状态	已出版 - 1 1月 2014
活动	2014 TREC Video Retrieval Evaluation, TRECVID 2014 - Orlando, 美国期限: 10 11月 2014 → 12 11月 2014

会议

会议	2014 TREC Video Retrieval Evaluation, TRECVID 2014
国家/地区	美国
市	Orlando
时期	10/11/14 → 12/11/14

其它文件与链接

链接到 Scopus 的出版物

引用此

@conference{881cfcbf84924eb6b9d5459ab6f8286b,

title = "A system for TRecViD med by MCIS",

abstract = "We designed a simple system for the 2014 TRECVID Multimedia Event Detection [1]. Except the videos provided by NIST, we only used the BVLC Reference CaffeNet model file distributed besides Caffe [2]. Our system follows the standard pipeline and consists two parts: feature extraction and classification. The feature extraction part is implemented by Caffe and the classification is implemented by LIBSVM [3]. Based on the results, we think that the contribution mainly comes from the feature extraction part. We learned that Convolutional Neural Networks (CNN) is a powerfully model and hope that a easy accessible spatio-temporal CNN model for videos will be available soon.",

author = "Yang Feng and Wanchen Sui and Xinxiao Wu",

note = "Publisher Copyright: {\textcopyright} 2020 2014 TREC Video Retrieval Evaluation. All rights reserved.; 2014 TREC Video Retrieval Evaluation, TRECVID 2014 ; Conference date: 10-11-2014 Through 12-11-2014",

year = "2014",

month = jan,

day = "1",

language = "English",

}

TY - CONF

T1 - A system for TRecViD med by MCIS

AU - Feng, Yang

AU - Sui, Wanchen

AU - Wu, Xinxiao

PY - 2014/1/1

Y1 - 2014/1/1

N2 - We designed a simple system for the 2014 TRECVID Multimedia Event Detection [1]. Except the videos provided by NIST, we only used the BVLC Reference CaffeNet model file distributed besides Caffe [2]. Our system follows the standard pipeline and consists two parts: feature extraction and classification. The feature extraction part is implemented by Caffe and the classification is implemented by LIBSVM [3]. Based on the results, we think that the contribution mainly comes from the feature extraction part. We learned that Convolutional Neural Networks (CNN) is a powerfully model and hope that a easy accessible spatio-temporal CNN model for videos will be available soon.

AB - We designed a simple system for the 2014 TRECVID Multimedia Event Detection [1]. Except the videos provided by NIST, we only used the BVLC Reference CaffeNet model file distributed besides Caffe [2]. Our system follows the standard pipeline and consists two parts: feature extraction and classification. The feature extraction part is implemented by Caffe and the classification is implemented by LIBSVM [3]. Based on the results, we think that the contribution mainly comes from the feature extraction part. We learned that Convolutional Neural Networks (CNN) is a powerfully model and hope that a easy accessible spatio-temporal CNN model for videos will be available soon.

UR - https://www.scopus.com/pages/publications/85203626439

M3 - Paper

AN - SCOPUS:85203626439

T2 - 2014 TREC Video Retrieval Evaluation, TRECVID 2014

Y2 - 10 November 2014 through 12 November 2014

ER -

A system for TRecViD med by MCIS

摘要

会议

其它文件与链接

学术指纹

引用此