Purpose: Recent research has been producing an important effort in encoding of digital image content. Most of the adopted paradigms only focus on local features and lack of information about location and relationships between them. Approach: To fill this gap, we propose a framework built on three cornerstones. First, the adoption of attributed relational scale-invariant feature transform regions graph, for image representation. Second, the application of a graph embedding model, to work in a simplified vector space, is performed. Finally, fast graph convolutional networks address classification task on a graph-based dataset representation. Results: The framework is evaluated on state of art object recognition datasets with uniform background. Conclusions: A wide experimental phase is performed through a comparison to well-known competitors.

中文翻译：

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FastGCN + ARSRGemb：一种用于对象识别的新颖框架

目的：最近的研究已经在数字图像内容的编码方面做出了重要的努力。大多数采用的范例仅关注局部特征，而缺乏有关位置和它们之间的关系的信息。方法：为了填补这一空白，我们提出了一个建立在三个基石上的框架。首先，采用属性关系尺度不变特征变换区域图进行图像表示。第二，执行图嵌入模型以在简化的矢量空间中工作。最后，快速图卷积网络处理基于图的数据集表示形式的分类任务。结果：该框架是在具有统一背景的先进物体识别数据集上进行评估的。结论：通过与知名竞争对手进行比较，进行了广泛的实验阶段。