With the rising prevalence of social media, coupled with the ease of sharing images, people with specific needs and applications such as known item search, multimedia question answering, etc., have st...
With the rapid growth of multimedia application technologies and network technologies, especially the proliferation of Web 2.0 and digital cameras, there has been an explosion of images and videos in ...
With the explosive growth of web videos on the Internet, it becomes challenging to efficiently browse hundreds or even thousands of videos. When searching an event query, users are often bewildered by...
With the explosive growth of multimedia contents on the internet, multimedia search has become more and more important. However, users are often bewildered by the vast quantity of information content ...
With the advent of video sharing websites, the amount of videos on the internet grows rapidly. Web video categorization is an efficient methodology for organizing the huge amount of videos. In this pa...
We submitted six interactive search runs for TRECVID2007, including 2 single user and 4 collaborative runs. In one single-user run (FXPAL MMA) the searchers had access to all resources including text ...
We propose a novel scheme to address video concept learning by leveraging social media, one that includes the selection of web training data and the transfer of subspace learning within a unified fram...
We describe our experiments for the High-level Feature Extraction (FE) and Search (SE) tasks. We submitted two automatic runs to the FE task, the first one (MMIS alexei) was based on a probabilistic a...
To exploit the hidden group structures of data and thus detect concepts in videos, this paper proposes a novel group sparse ensemble learning approach based on Automatic Group Sparse Coding (AutoGSC)....
This work presents a novel sparse ensemble learning scheme for concept detection in videos. The proposed ensemble first exploits a sparse non-negative matrix factorization (NMF) process to represent d...
This paper reports our experiments for TRECVID 2008 tasks: high level feature extraction, search and contentbased copy detection. For the high level feature extraction task, we use the baseline featur...
This paper presents our approaches and results of the four TRECVID 2008 tasks we participated in: high-level feature extraction, automatic video search, video copy detection, and rushes summarization....
This paper focuses on the comparison between two fusion methods, namely early fusion and late fusion. The former fusion is carried out at kernel level, also known as multiple kernel learning, and in t...
This paper describes our system for auto search and inter- active search in the known-item search (KIS) task in TRECVID 2010. KIS task aims to find an unique video answer for each text query. The shif...
This paper describes our experiments for the high level feature extraction task in TRECVid 2008. We submitted the following six runs: • A jrs1 1: Baseline, early fusion, tuned SVMs. • A jrs2 2: Early ...
The overwhelming amount of multimedia entities shared over the web has given rise to the need for semantic identification and classification of these entities. Numerous research efforts have tackled t...
The bag-of-words (BoW) has been widely regarded as the most successful algorithms for content-based image related tasks, such as large scale image retrieval, classification, and object categorization....
Tags associated with web videos play a crucial role in organizing and accessing large-scale video collections. However, the raw tag list (RawL) is usually incomplete, imprecise and unranked, which red...
Surveillance Event Detection Semantic event detection in the huge amount of surveillance video in both retrospective and real-time styles is essential to a variety of higher-level applications in the ...
Shot boundary detection The shot boundary detection system in 2007 is basically the same as that of last year. We make three major modifications in the system of this year. First, CUT detector and GT ...