Abstract. 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.

中文翻译：

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基于矩阵相关的图像检索多视图特征融合优化方法

摘要。多视图学习是图像处理或大数据分析等领域中一种重要的特征融合方法，广泛应用于特征融合。确定如何从多个视图整合兼容和互补的信息是一项至关重要且具有挑战性的任务。我们提出了一种基于矩阵相关的图像检索多视图特征融合优化方法。该方法首先从图像中提取四个视图特征（Gist、颜色直方图、定向梯度金字塔直方图和多趋势结构描述符）。然后通过局部嵌入将这些特征分别转换为不同的图拉普拉斯矩阵。第三，使用矩阵相关统计构建多视图特征交替优化过程，该过程自适应地将不同视图特征图组合成统一的低维嵌入。最后，融合特征用于图像检索实验。各种实验结果表明，该算法是一种有效的方法。