In the designing field, designers usually retrieve the images for reference according to product attributes when designing new proposals. To obtain the attributes of the product, the designers take lots of time and effort to collect product images and annotate them with multiple labels. However, the labels of product images represent the concept of subjective perception, which makes the multi-label learning more challenging to imitate the human aesthetic rather than discriminate the appearance. In this paper, a Feature Correlation Learning (FCL) network is proposed to solve this problem by exploiting the potential feature correlations of product images. Given a product image, the FCL network calculates the features of different levels and their correlations via gram matrices. The FCL is aggregated with the DenseNet to predict the labels of the input product image. The proposed method is compared with several outstanding multi-label learning methods, as well as DenseNet. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts for multi-label learning problem of product image data.

在设计领域，设计人员通常在设计新建议时根据产品属性检索图像以供参考。为了获得产品的属性，设计人员花费大量时间和精力来收集产品图像并使用多个标签对其进行注释。然而，产品图像的标签代表了主观感知的概念，这使得多标签学习在模仿人类美学而不是区分外观方面更具挑战性。本文提出了一种特征相关学习（FCL）网络，通过利用产品图像的潜在特征相关性来解决这一问题。给定一个产品图像，FCL网络通过克矩阵计算不同级别的特征及其相关性。FCL与DenseNet聚合在一起，以预测输入产品图像的标签。将该方法与几种出色的多标签学习方法以及DenseNet进行了比较。实验结果表明，该方法优于产品图像数据多标签学习问题的最新技术。