Abstract. This report describes video concept detection using Support Vector Machine (SVM) over TRECVID 2007 corpus. We perform the experiments on low-level features extraction, data preparation and classification procedure. Through analyzing the characteristics of the TRECVID 2007 data set, we mainly focus on data preparation for training concept detectors, as well as the preparation of auxiliary training data by using TRECVID 2005 data
Analysis and detection of complex events in videos require a se-mantic representation of the video c...
The video retrieval system we developed for TRECVID 2012 mainly involves the semantic indexing task ...
According to some current thinking, a very large number of semantic concepts could provide researche...
In this paper we summarize our TRECVID 2014 video retrieval experiments. The MediaMill team particip...
We provide concept detection scores for the IACC.3 dataset (600 hr internet archive videos), which i...
This article presents a new system for automatically extract-ing high-level video concepts. The nove...
Video concept detection aims to find videos that show a certain event described as a high-level conc...
This paper presents the systems used by CLIPS-IMAG laboratory. We participated to shot seg-mentation...
In this paper we summarize our TRECVID 2015 video recognition experiments. We participated in three ...
Successful and effective content-based access to digital video requires fast, accurate and scalable...
Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxil...
<p>We provide concept detection scores for the MED16train dataset which is used at the TRECVID Multi...
In this paper we describe our TRECVID 2012 video retrieval experiments. The MediaMill team participa...
Concept-based video representation has proven to be effective in complex event detection. However, e...
This paper describes the details of our systems for feature extraction and search tasks of TRECVID-2...
Analysis and detection of complex events in videos require a se-mantic representation of the video c...
The video retrieval system we developed for TRECVID 2012 mainly involves the semantic indexing task ...
According to some current thinking, a very large number of semantic concepts could provide researche...
In this paper we summarize our TRECVID 2014 video retrieval experiments. The MediaMill team particip...
We provide concept detection scores for the IACC.3 dataset (600 hr internet archive videos), which i...
This article presents a new system for automatically extract-ing high-level video concepts. The nove...
Video concept detection aims to find videos that show a certain event described as a high-level conc...
This paper presents the systems used by CLIPS-IMAG laboratory. We participated to shot seg-mentation...
In this paper we summarize our TRECVID 2015 video recognition experiments. We participated in three ...
Successful and effective content-based access to digital video requires fast, accurate and scalable...
Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxil...
<p>We provide concept detection scores for the MED16train dataset which is used at the TRECVID Multi...
In this paper we describe our TRECVID 2012 video retrieval experiments. The MediaMill team participa...
Concept-based video representation has proven to be effective in complex event detection. However, e...
This paper describes the details of our systems for feature extraction and search tasks of TRECVID-2...
Analysis and detection of complex events in videos require a se-mantic representation of the video c...
The video retrieval system we developed for TRECVID 2012 mainly involves the semantic indexing task ...
According to some current thinking, a very large number of semantic concepts could provide researche...