This paper introduces a deep learning method for image classification that leverages knowledge formalized as a graph created from information represented by pairs attribute/value. The proposed method investigates a loss function that adaptively combines the classical cross-entropy commonly used in deep learning with a novel penalty function. The novel loss function is derived from the representation of nodes after embedding the knowledge graph and incorporates the proximity between class and image nodes. Its formulation enables the model to focus on identifying the boundary between the most challenging classes to distinguish. Experimental results on several image databases demonstrate improved performance compared to state-of-the-art methods, including classical deep learning algorithms and recent algorithms that incorporate knowledge represented by a graph.

中文翻译：

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基于知识图谱的图像分类

本文介绍了一种用于图像分类的深度学习方法，该方法利用形式化为由属性/值对表示的信息创建的图的知识。该方法研究了一种损失函数，该损失函数自适应地将深度学习中常用的经典交叉熵与新颖的惩罚函数相结合。新的损失函数是从嵌入知识图谱后的节点表示导出的，并结合了类和图像节点之间的邻近性。它的公式使模型能够专注于识别最具挑战性的类别之间的边界以进行区分。几个图像数据库的实验结果表明，与最先进的方法相比，包括经典的深度学习算法和融合了图表示的知识的最新算法，性能得到了提高。