PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Redundant Class-Dependence Feature Analysis Based on Correlation Filters Using FRGC2.0 Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Discriminant analysis in correlation similarity measure space
Proceedings of the 24th international conference on Machine learning
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
ClassView: hierarchical video shot classification, indexing, and accessing
IEEE Transactions on Multimedia
Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval
IEEE Transactions on Multimedia
Effective Image Retrieval Based on Hidden Concept Discovery in Image Database
IEEE Transactions on Image Processing
Optimizing multimedia retrieval using multimodal fusion and relevance feedback techniques
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
A unified framework for multimodal retrieval
Pattern Recognition
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Correlation measure is a new hot topic in multimedia retrieval compared to distance metric like Euclidean and Mahalanobis distances. However, most correlation learning algorithms focused on multimedia data of single modality. For heterogeneous multi-modal data of different modalities correlation learning is more complicated. In this paper, we analyze multi-modal correlation among text, image and audio to understand underlying semantics for multi-modal retrieval. First, Kernel Canonical Correlation is used to build a kernel space where global inter-media correlation is analyzed; based on local geometrical topology in the kernel space a weighted graph and corresponding affinity matrix are formed for data and correlation representation; then correlation ranking is used to generate retrieval results; we also provide active learning strategies in relevance feedback to improve retrieval results. Experiment and comparison results are encouraging and show that the performance of our approach is effective.