IEEE Trans Image Process. 2013 Dec;22(12):5071-84. doi: 10.1109/TIP.2013.2278465. Epub 2013 Aug 14.
Recognizing aerial image categories is useful for scene annotation and surveillance. Local features have been demonstrated to be robust to image transformations, including occlusions and clutters. However, the geometric property of an aerial image (i.e., the topology and relative displacement of local features), which is key to discriminating aerial image categories, cannot be effectively represented by state-of-the-art generic visual descriptors. To solve this problem, we propose a recognition model that mines graphlets from aerial images, where graphlets are small connected subgraphs reflecting both the geometric property and color/texture distribution of an aerial image. More specifically, each aerial image is decomposed into a set of basic components (e.g., road and playground) and a region adjacency graph (RAG) is accordingly constructed to model their spatial interactions. Aerial image categories recognition can subsequently be casted as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by comparing all their respective graphlets. Because the number of graphlets is huge, we derive a manifold embedding algorithm to measure different-sized graphlets, after which we select graphlets that have highly discriminative and low redundancy topologies. Through quantizing the selected graphlets from each aerial image into a feature vector, we use support vector machine to discriminate aerial image categories. Experimental results indicate that our method outperforms several state-of-the-art object/scene recognition models, and the visualized graphlets indicate that the discriminative patterns are discovered by our proposed approach.
识别航空图像类别对于场景标注和监控非常有用。局部特征已被证明对图像变换具有鲁棒性，包括遮挡和混叠。然而，航空图像的几何性质（即局部特征的拓扑和相对位移）是区分航空图像类别的关键，无法被最新的通用视觉描述符有效地表示。为了解决这个问题，我们提出了一种从航空图像中挖掘图元的识别模型，其中图元是反映航空图像几何性质和颜色/纹理分布的小连通子图。更具体地说，每个航空图像都被分解为一组基本组件（例如，道路和操场），并相应地构建一个区域邻接图（RAG）来模拟它们的空间相互作用。航空图像类别识别随后可以被转化为 RAG 到 RAG 的匹配。基于图论，通过比较它们各自的所有图元来进行 RAG 到 RAG 的匹配。由于图元的数量巨大，我们推导出一种流形嵌入算法来度量不同大小的图元，然后选择具有高度判别性和低冗余拓扑的图元。通过将每个航空图像的选定图元量化为特征向量，我们使用支持向量机来区分航空图像类别。实验结果表明，我们的方法优于几种最新的物体/场景识别模型，可视化的图元表明，我们提出的方法发现了有区分性的模式。
