In this paper we present the methods and visualizations used in the MediaMill video search engine. The basis for the engine is a semantic indexing process which derives a lexicon of 101 concepts. To support the user in navigating the collection, the system defines a visual similarity space, a semantic similarity space, a semantic thread space, and browsers to explore them. The search system is evaluated within the TRECVID benchmark. We obtain a top-3 result for 19 out of 24 search topics. In addition, we obtain the highest mean average precision of all search participants.
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...
In this paper we describe our TRECVID 2012 video retrieval experiments. The MediaMill team participa...
In this paper we present the methods and visualizations used in the MediaMill video search engine. T...
In this paper we present our Mediamill video search engine. The basis for the engine is a semantic i...
We combine in this paper automatic learning of a large lexicon of semantic concepts with traditional...
In this technical demonstration we showcase the current version of the MediaMill system, a search en...
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 2007 experiments. The MediaMill team participated in two tasks...
In this technical demonstration we show the current version of the MediaMill system, a search engine...
In this paper we describe our TRECVID 2011 video retrieval experiments. The MediaMill team participa...
In this paper we describe our TRECVID 2009 video retrieval experiments. The MediaMill team participa...
We briefly describe “CuVid, ” Columbia University’s video search engine, a system that enables seman...
In this paper we describe our TRECVID 2008 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...
In this paper we describe our TRECVID 2012 video retrieval experiments. The MediaMill team participa...
In this paper we present the methods and visualizations used in the MediaMill video search engine. T...
In this paper we present our Mediamill video search engine. The basis for the engine is a semantic i...
We combine in this paper automatic learning of a large lexicon of semantic concepts with traditional...
In this technical demonstration we showcase the current version of the MediaMill system, a search en...
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 2007 experiments. The MediaMill team participated in two tasks...
In this technical demonstration we show the current version of the MediaMill system, a search engine...
In this paper we describe our TRECVID 2011 video retrieval experiments. The MediaMill team participa...
In this paper we describe our TRECVID 2009 video retrieval experiments. The MediaMill team participa...
We briefly describe “CuVid, ” Columbia University’s video search engine, a system that enables seman...
In this paper we describe our TRECVID 2008 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...
In this paper we describe our TRECVID 2012 video retrieval experiments. The MediaMill team participa...