Learning Semantic Correlation of Web Images and Text with Mixture of Local Linear Mappings

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 more complex nonlinear one. To handle the sparseness of data representation, we introduce the constraints of sparseness and non-negativeness into the approach. MLLM is with good interpretability due to its explicit closed form and concept-related local components, and it avoids the determination of capacity that is often considered for nonlinear transformations. Experimental results demonstrate the effectiveness of the proposed approach.