Heterogeneous multimedia data semantics mining using content and location context
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Personalized Multimedia Retrieval in CADAL Digital Library
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A Unified Indexing Structure for Efficient Cross-Media Retrieval
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
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
Cross-media retrieval using query dependent search methods
Pattern Recognition
Combining location and feature information for multimedia retrieval
International Journal of Computer Applications in Technology
A new approach for texture classification in CBIR
International Journal of Computer Applications in Technology
Cartoon synthesis using constrained spreading activation network
Multimedia Tools and Applications
VisionGo: Towards video retrieval with joint exploration of human and computer
Information Sciences: an International Journal
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
Effective heterogeneous similarity measure with nearest neighbors for cross-media retrieval
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
A novel multi-modal integration and propagation model for cross-media information retrieval
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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
A semantic model for cross-modal and multi-modal retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A low rank structural large margin method for cross-modal ranking
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Parallel field alignment for cross media retrieval
Proceedings of the 21st ACM international conference on Multimedia
Cross-media semantic representation via bi-directional learning to rank
Proceedings of the 21st ACM international conference on Multimedia
Multi-view hypergraph learning by patch alignment framework
Neurocomputing
A framework for key-frame selection based on relevance feedback
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
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Although multimedia objects such as images, audios and texts are of different modalities, there are a great amount of semantic correlations among them. In this paper, we propose a method of transductive learning to mine the semantic correlations among media objects of different modalities so that to achieve the cross-media retrieval. Cross-media retrieval is a new kind of searching technology by which the query examples and the returned results can be of different modalities, e.g., to query images by an example of audio. First, according to the media objects features and their co-existence information, we construct a uniform cross-media correlation graph, in which media objects of different modalities are represented uniformly. To perform the cross-media retrieval, a positive score is assigned to the query example; the score spreads along the graph and media objects of target modality or MMDs with the highest scores are returned. To boost the retrieval performance, we also propose different approaches of long-term and short-term relevance feedback to mine the information contained in the positive and negative examples.