We describe a baseline system for the VideoCLEF Vid2RSS task. The system uses an unaltered off-the-shelf Information Retrieval system. ASR content is indexed using default stemming and stopping methods. The subject categories are populated by using the category label as a query on the collection, and assigning the retrieved items to that particular category. We describe the results of the system and provide some high-level analysis of its performance
Many research groups worldwide are now investigating techniques which can support information retrie...
As data storage capacities grow to nearly unlimited sizes thanks to ever ongoing hardware and softwa...
We described Dublin City University (DCU)'s participation in the Search sub-task of the Search and H...
We describe a baseline system for the VideoCLEF Vid2RSS task. The system uses an unaltered off-the-s...
VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access...
The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of a...
Dublin City University participated in the Feature Extraction task and the Search task of the TREC-2...
The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of a...
VideoCLEF is a new track for the CLEF 2008 campaign. This track aims to develop and evaluate tasks ...
The University of Amsterdam (UAms) team carried out the Vid2RSS classification task, the primary sub...
This study discusses the findings of an evaluation study on the performance of a multimedia multimod...
This paper reports on the setup and evaluation of robust speech recognition system parts, geared tow...
We present an exploratory study of the retrieval of semiprofessional user-generated Internet video. ...
The Multimodal Video Search by Examples (MVSE) project investigates using video clips as the query t...
This paper describes the participation of the University of Twente team at the Rich Text Retrieval T...
Many research groups worldwide are now investigating techniques which can support information retrie...
As data storage capacities grow to nearly unlimited sizes thanks to ever ongoing hardware and softwa...
We described Dublin City University (DCU)'s participation in the Search sub-task of the Search and H...
We describe a baseline system for the VideoCLEF Vid2RSS task. The system uses an unaltered off-the-s...
VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access...
The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of a...
Dublin City University participated in the Feature Extraction task and the Search task of the TREC-2...
The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of a...
VideoCLEF is a new track for the CLEF 2008 campaign. This track aims to develop and evaluate tasks ...
The University of Amsterdam (UAms) team carried out the Vid2RSS classification task, the primary sub...
This study discusses the findings of an evaluation study on the performance of a multimedia multimod...
This paper reports on the setup and evaluation of robust speech recognition system parts, geared tow...
We present an exploratory study of the retrieval of semiprofessional user-generated Internet video. ...
The Multimodal Video Search by Examples (MVSE) project investigates using video clips as the query t...
This paper describes the participation of the University of Twente team at the Rich Text Retrieval T...
Many research groups worldwide are now investigating techniques which can support information retrie...
As data storage capacities grow to nearly unlimited sizes thanks to ever ongoing hardware and softwa...
We described Dublin City University (DCU)'s participation in the Search sub-task of the Search and H...