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Learning Semantic Correlation of Web Images and Text with Mixture of Local Linear Mappings
2015
Proceedings of the 23rd ACM international conference on Multimedia - MM '15
This paper proposes a new approach, called mixture of local linear mappings (MLLM ), to the modeling of semantic correlation between web images and text. We consider that close examples generally represent a uniform concept and can be supposed to be locally transformed based on a linear mapping into the feature space of another modality. Thus, we use a mixture of local linear transformations, each local component being constrained by a neighborhood model into a finite local space, instead of a
doi:10.1145/2733373.2806331
dblp:conf/mm/DuY15
fatcat:l4r5e5xytvhfverk235ffylpdu