International Journal of Computer Vision
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Statistical Shape Analysis: Clustering, Learning, and Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Multi-graph enabled active learning for multimodal web image retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Cross-modal correlation learning for clustering on image-audio dataset
Proceedings of the 15th international conference on Multimedia
Budget Semi-supervised Learning
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Cross-media retrieval using query dependent search methods
Pattern Recognition
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
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
Cross-Media semantics mining based on sparse canonical correlation analysis and relevance feedback
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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An important trend in multimedia semantic understanding is the utilization and support of multimodal data which are heterogeneous in low-level features, such as image and audio. The main challenge is how to measure different kinds of correlations among multimodal data. In this paper, we propose a novel approach to boost multimodal semantic understanding from local and global perspectives. First, cross-media correlation between images and audio clips is estimated with Kernel Canonical Correlation Analysis; secondly, a multimodal graph is constructed to enable global correlation propagation with adapted intra-media similarity; then cross-media retrieval algorithm is discussed as an application of our approach. A prototype system is developed to demonstrate the feasibility and capability. Experimental results are encouraging and show that the performance of our approach is effective.