基于分层区域合并的自然场景理解

针对自然场景理解问题，利用图像中的层次结构，提出了一种基于分层合并的图像场景理解方法。该方法通过不断合并相邻区域，直到合并出图像中的各个对象为止；最终得到一个合并森林，森林里的每棵树对应图像中的一个对象。我们设计了一个机器学习模型来描述合并过程、一种贪心推理方法来求解最优的合并森林以及一种基于最大间隔的学习方法来训练模型中的参数，同时采用分层聚类来进行参数的初始化。本文方法可以看成为图像语义理解而设计的一种深度学习方法。实验效果令人满意。

Parsing Natural Scenes Based on Hierarchical Region Merge

The goal of scene understanding is to recognize what objects in the image and where the objects are located. Hierarchical structure is commonly found in the natural scene images. This structure not only can help us to identity the objects but also how the small units interact to form the whole objects. Our algorithm is based on the level structure. We merge the neighboring segments continuously until they combined into the whole object. The result is a forest which contains several trees, one tree commonly represents one object. We introduce a machine learning model to describe the merge process, greedy inference to compute the best merge trees, and the max margin to learn the parameters. We cluster the segments features to initialize the parameter. The experiment result could be accepted.