Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Multimedia content processing through cross-modal association
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Approximate similarity search in metric spaces using inverted files
Proceedings of the 3rd international conference on Scalable information systems
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Multi-modal Correlation Modeling and Ranking for Retrieval
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Segmentation, indexing, and retrieval for environmental and natural sounds
IEEE Transactions on Audio, Speech, and Language Processing
A 3D Shape Retrieval Framework Supporting Multimodal Queries
International Journal of Computer Vision
Measuring multi-modality similarities via subspace learning for cross-media retrieval
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
SHREC'10 track: generic 3D warehouse
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
I-SEARCH: a unified framework for multimodal search and retrieval
The Future Internet
| Hi-index | 0.00 |
This paper introduces a novel approach for search and retrieval of multimedia content. The proposed framework retrieves multiple media types simultaneously, namely 3D objects, 2D images and audio files, by utilizing an appropriately modified manifold learning algorithm. The latter, which is based on Laplacian Eigenmaps, is able to map the mono-modal low-level descriptors of the different modalities into a new low-dimensional multimodal feature space. In order to accelerate search and retrieval and make the framework suitable even for large-scale applications, a new multimedia indexing scheme is adopted. The retrieval accuracy of the proposed method is further improved through relevance feedback, which enables users to refine their queries by marking the retrieved results as relevant or non-relevant. Experiments performed on a multimodal dataset demonstrate the effectiveness and efficiency of our approach. Finally, the proposed framework can be easily extended to involve as many heterogeneous modalities as possible.