Scene parsing aims to recognize the object category of every pixel in scene images, and it plays a central role in image content understanding and computer vision applications. However, accurate scene parsing from unconstrained real-world data is still a challenging task. In this paper, we present the non-parametric Spatially Constrained Local Prior (SCLP) for scene parsing on realistic data. For a given query image, the non-parametric SCLP is learnt by first retrieving a subset of most similar training images to the query image and then collecting prior information about object co-occurrence statistics between spatial image blocks and between adjacent superpixels from the retrieved subset. The SCLP is powerful in capturing both long- and short-range context about inter-object correlations in the query image and can be effectively integrated with traditional visual features to refine the classification results. Our experiments on the SIFT Flow and PASCAL-Context benchmark datasets show that the non-parametric SCLP used in conjunction with superpixel-level visual features achieves one of the top performance compared with state-of-the-art approaches.

场景解析旨在识别场景图像中每个像素的对象类别，并且在图像内容理解和计算机视觉应用中起着核心作用。但是，从不受约束的实际数据中进行准确的场景解析仍然是一项艰巨的任务。在本文中，我们提出了用于实际数据场景解析的非参数空间约束局部先验（SCLP）。对于给定的查询图像，通过首先将最相似的训练图像的子集检索到查询图像，然后从检索的子集中收集有关空间图像块之间以及相邻超像素之间的对象共现统计的先验信息，来学习非参数SCLP。 。SCLP可以强大地捕获有关查询图像中对象间相关性的长距离和短距离上下文，并且可以有效地与传统视觉功能集成以细化分类结果。我们在SIFT Flow和PASCAL-Context基准数据集上的实验表明，与最先进的方法相比，与超像素级视觉特征结合使用的非参数SCLP可以实现最佳性能。