Algorithms for clustering data
Algorithms for clustering data
IIiX Proceedings of the 1st international conference on Information interaction in context
Using score distributions for query-time fusion in multimediaretrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Promoting emergence in information discovery by representing collections with composition
Proceedings of the 6th ACM SIGCHI conference on Creativity & cognition
FacetBrowser: a user interface for complex search tasks
MM '08 Proceedings of the 16th ACM international conference on Multimedia
An efficient indexing structure for multimedia data
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Annotation of heterogeneous multimedia content using automatic speech recognition
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Web-based semantic browsing of video collections using multimedia ontologies
Proceedings of the international conference on Multimedia
University of Glasgow at ImageCLEFPhoto 2009: optimising similarity and diversity in image retrieval
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Semantic browsing in large scale videos collection
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Video exploration tool based on semantic network
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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In this paper, we present a novel video search interface based on the concept of aspect browsing. The proposed strategy is to assist the user in exploratory video search by actively suggesting new query terms and video shots. Our approach has the potential to narrow the "Semantic Gap" issue by allowing users to explore the data collection. First, we describe a clustering technique to identify potential aspects of a search. Then, we use the results to propose suggestions to the user to help them in their search task. Finally, we analyse this approach by exploiting the log files and the feedbacks of a user study.