Multidimensional access methods
ACM Computing Surveys (CSUR)
MOSAIC: a fast multi-feature image retrieval system
Data & Knowledge Engineering
ACM Computing Surveys (CSUR)
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A review of text and image retrieval approaches for broadcast news video
Information Retrieval
Challenges and techniques for effective and efficient similarity search in large video databases
Proceedings of the VLDB Endowment
Similarity Search: The Metric Space Approach
Similarity Search: The Metric Space Approach
TempoM2: a multi feature index structure for temporal video search
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Adapting metric indexes for searching in multi-metric spaces
Multimedia Tools and Applications
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Efficient and effective handling of video documents depends on the availability of indexes. Manual indexing is unfeasible for large video collections. Video combines different types of data from different modalities. Using information from multiple modalities may result in a more robust and accurate video retrieval. Therefore, effective indexing for video retrieval requires a multimodal approach in which either the most appropriate modality is selected or the different modalities are used in collaborative fashion. This paper presents a new metric access method -- Slim2-tree -- which combines information from multiple modalities within a single index structure for video retrieval. Experimental studies on a large real dataset show the video similarity search performance of the proposed technique. Additionally, we present experiments comparing our method against state-of-the-art of multimodal solutions. Comparative test results demonstrate that our technique improves the performance of video similarity queries.