Multiview learning is an important method and widely used for feature fusion in the fields of image process or big data analysis. Determining how to integrate compatible and complementary information from multiple views is a crucial and challenging task. We present a multiview feature fusion optimization method for image retrieval based on matrix correlation. This method first extracts four view features (Gist, histogram of color, pyramid histogram of oriented gradients, and multitrend structure descriptor) from the image. Then these features are, respectively, converted to different graph Laplacian matrices through local embedding. Third, a multiview feature alternating optimization process is constructed using matrix correlation statistics that adaptively combines the different view feature maps to a unified, low-dimensional embedding. Finally, the fusion feature is used for image retrieval experiments. Various experimental results show that the proposed algorithm is an effective method.