Dataset for image classification with knowledge

Deep learning applied to raw data has demonstrated outstanding image classification performance, mainly when abundant data is available. However, performance significantly degrades when a substantial volume of data is unavailable. Furthermore, in situations where distinguishing between distinct classes is challenging, such as in fine-grained image classification, deep architectures struggle to achieve satisfactory performance levels. Utilizing a priori knowledge alongside raw data can enhance image classification in demanding scenarios. Nevertheless, only a limited number of image classification datasets given with a priori knowledge are currently available, thereby restricting research efforts in this field. This paper introduces innovative datasets for the classification problem that integrate a priori knowledge. These datasets are built from existing data typically employed for multilabel multiclass classification or object detection. Frequent closed itemset mining is used to build classes and their corresponding attributes (e.g. presence of an object in an image) and then to extract a priori knowledge expressed by rules on these attributes. The algorithm for generating rules is described.将深度学习应用于原始数据时，已展现出优异的图像分类性能，这在数据量充足的场景中尤为突出。然而，当缺乏足量数据时，其性能会显著下滑。此外，在区分不同类别存在较大难度的场景（如细粒度图像分类任务）中，深度模型架构往往难以达到令人满意的性能水准。将先验知识与原始数据结合使用，可在高难度场景中提升图像分类的整体效果。然而，当前带有先验知识的图像分类数据集数量十分有限，这极大制约了该领域的研究推进。本文提出了集成先验知识的新型图像分类数据集。这些数据集均基于常用于多标签多分类任务或目标检测任务的现有数据构建而成。研究采用频繁闭项集挖掘（Frequent Closed Itemset Mining）方法构建类别及其对应属性（如图像中某物体的存在性），并基于这些属性提取以规则形式表达的先验知识。本文对该规则生成算法进行了详细阐述。