Semantic concept classification is a critical task for content-based video retrieval. Traditional methods of machine learning focus on increasing the accuracy of classifiers or models, and face the pr...
Retrieving images with multiple features is an active research topic on boosting the performance of existing content-based image retrieval methods. The promising bags-of-words (BoW) models involve mul...
Recent work in visual retrieval shows that bag-of-features (BoF) has appeared promising for object recognition and categorization. Local descriptors such as SIFT have shown impressive results on objec...
Realistic human action recognition in videos has been a useful yet challenging task. Video shots of same actions may present huge intra-class variations in terms of visual appearance, kinetic patterns...
Real-time applications are usually well-defined and operate based on a particular system model. However, in practical scenarios, the applications can perform differently because of the uncertainties i...
Product annotation in videos is of great importance for video browsing, search, and advertisement. However, most of the existing automatic video annotation research focuses on the annotation of high-l...
One of the main challenges in interactive concept-based video search is the problem of insufficient relevant samples, especially for queries with complex semantics. In this paper, “related samples” ar...
Multi-modality, the unique and important property of video data, is typically ignored in existing video adaptation processes. To solve this problem, we propose a novel approach, named multi-modality t...
Latent Dirichlet allocation (LDA) topic model has taken a center stage in multimedia information retrieval, for example, LDA model was used by several participants in the recent TRECVid evaluation “Se...
In this report, we describe the approaches and experiments on TRECVid 2013 video concept detection conducted by NTT Media Intelligence Laboratories in collaboration with Dalian University of Technolog...
In this paper, we specially propose a hierarchical framework for movie content analysis. The purpose of our work is trying to realize computerspsila understanding for movie content, especially ldquowh...
In this paper, we review 300 references on video retrieval, indicating when text-only solutions are unsatisfactory and showing the promising alternatives which are in majority concept-based. Therefore...
In this paper, we propose a human-centered framework, “Watching, Thinking, Reacting”, for movie content analysis. The framework consists of a hierarchy of three levels. The low level represents human ...
In this paper, we highlight the use of multimedia technology in generating intrinsic summaries of tourism related information. The system utilizes an automated process to gather, filter and classify i...
In this paper, we describe the TRECVid 2011 semantic indexing system first developed at the NTT Cyber Communication Laboratory Group in collaboration with Zhejiang University. In addition to adopting ...
In this paper, we describe the IBM Research system for indexing, analysis, and retrieval of video as applied to the TREC-2007 video retrieval benchmark. This year, focus of the system improvement was ...
In this paper we describe our experiments in the automatic search task of TRECVid 2007. For this we have implemented a new video search technique based on SIFT features and manual annotation. We submi...
In this paper we describe our TRECVID 2009 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interactive search. Starting point for ...
In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interactive search. Rather than continu...
In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interac- tive search. Rather than conti...