In this technical demonstration we show the current version of the MediaMill system, a search engine that facilitates access to news video archives at a semantic level. The core of the system is a lexicon of 436 automatically detected semantic concepts. To handle such a large lexicon in retrieval, an engine is developed which automatically selects a set of relevant concepts based on the textual query and example images. The result set can be browsed easily to obtain the final result for the query
Abstract — In this paper, we propose an automatic video retrieval method based on high-level concept...
In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participa...
In this paper we describe our TRECVID 2011 video retrieval experiments. The MediaMill team participa...
In this technical demonstration we showcase the current version of the MediaMill system, a search en...
We combine in this paper automatic learning of a large lexicon of semantic concepts with traditional...
In this paper we present our Mediamill video search engine. The basis for the engine is a semantic i...
In this paper we describe our TRECVID 2007 experiments. The MediaMill team participated in two tasks...
In this paper we present the methods and visualizations used in the MediaMill video search engine. T...
In this paper we present the methods and visualizations used in the MediaMill video search engine. T...
In this paper we describe the current performance of our MediaMill system as presented in the TRECVI...
In this paper we describe our TRECVID 2006 experiments. The MediaMill team participated in two tasks...
In this paper we describe our TRECVID 2009 video retrieval experiments. The MediaMill team participa...
This year the UvA-MediaMill team participated in the Feature Extraction and Search Task. We develope...
In this paper we describe our TRECVID 2009 video re- trieval experiments. The MediaMill team partici...
We briefly describe “CuVid, ” Columbia University’s video search engine, a system that enables seman...
Abstract — In this paper, we propose an automatic video retrieval method based on high-level concept...
In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participa...
In this paper we describe our TRECVID 2011 video retrieval experiments. The MediaMill team participa...
In this technical demonstration we showcase the current version of the MediaMill system, a search en...
We combine in this paper automatic learning of a large lexicon of semantic concepts with traditional...
In this paper we present our Mediamill video search engine. The basis for the engine is a semantic i...
In this paper we describe our TRECVID 2007 experiments. The MediaMill team participated in two tasks...
In this paper we present the methods and visualizations used in the MediaMill video search engine. T...
In this paper we present the methods and visualizations used in the MediaMill video search engine. T...
In this paper we describe the current performance of our MediaMill system as presented in the TRECVI...
In this paper we describe our TRECVID 2006 experiments. The MediaMill team participated in two tasks...
In this paper we describe our TRECVID 2009 video retrieval experiments. The MediaMill team participa...
This year the UvA-MediaMill team participated in the Feature Extraction and Search Task. We develope...
In this paper we describe our TRECVID 2009 video re- trieval experiments. The MediaMill team partici...
We briefly describe “CuVid, ” Columbia University’s video search engine, a system that enables seman...
Abstract — In this paper, we propose an automatic video retrieval method based on high-level concept...
In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participa...
In this paper we describe our TRECVID 2011 video retrieval experiments. The MediaMill team participa...