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

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Publication status	Published - 2020
Event	2014 TREC Video Retrieval Evaluation, TRECVID 2014 - Orlando, United States Duration: 10 Nov 2014 → 12 Nov 2014

Conference

Conference	2014 TREC Video Retrieval Evaluation, TRECVID 2014
Country/Territory	United States
City	Orlando
Period	10/11/14 → 12/11/14

Cite this

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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 = "2020",

language = "English",

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TY - CONF

T1 - A system for TRecViD med by MCIS

AU - Feng, Yang

AU - Sui, Wanchen

AU - Wu, Xinxiao

PY - 2020

Y1 - 2020

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 - http://www.scopus.com/inward/record.url?scp=85085040901&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85085040901

T2 - 2014 TREC Video Retrieval Evaluation, TRECVID 2014

Y2 - 10 November 2014 through 12 November 2014

ER -

A system for TRecViD med by MCIS

Abstract

Conference

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