论文标题
FastGCN+Arsrgemb：一个新颖的对象识别框架
FastGCN+ARSRGemb: a novel framework for object recognition
论文作者
论文摘要
近年来，研究一直在努力编码数字图像内容。大多数采用的范式仅关注本地特征，并且缺乏有关位置及其之间关系的信息。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，采用了图像表示形式的ARSRG（归因为归因于关系筛分（比例不变特征变换）区域）。其次，应用了在简化的向量空间中工作的图形嵌入模型。最后，快速图形卷积网络在基于图的数据集表示上执行分类阶段。该框架通过广泛的实验阶段对最先进的对象识别数据集进行了评估，并与知名竞争者进行了比较。
In recent years research has been producing an important effort to encode the digital image content. Most of the adopted paradigms only focus on local features and lack in information about location and relationships between them. To fill this gap, we propose a framework built on three cornerstones. First, ARSRG (Attributed Relational SIFT (Scale-Invariant Feature Transform) regions graph), for image representation, is adopted. Second, a graph embedding model, with purpose to work in a simplified vector space, is applied. Finally, Fast Graph Convolutional Networks perform classification phase on a graph based dataset representation. The framework is evaluated on state of art object recognition datasets through a wide experimental phase and is compared with well-known competitors.
