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.
翻译:场景图象中的每个像素对象类别, 以及它在图像内容理解和计算机视觉应用中发挥着核心作用。 但是, 从不受限制的现实世界数据中准确的场景分解仍是一项艰巨的任务。 在本文中, 我们展示了对现实数据进行场景分解的非对数空间封闭的本地先行( SCLP ) 。 对于给定的查询图像, 非参数 SCLP 是先从查询图像中提取一组最相似的培训图像, 然后再收集空间图像区块之间和从回收的子集相邻的超像素之间关于对象共见点统计的先前信息。 SCLP 在捕捉取查询图像中跨对象相关性的长程和短程背景方面都非常强大, 并且可以有效地与传统的视觉特征整合, 以完善分类结果。 我们关于 SIFT Flow 和 PCAL- Context 基准数据集的实验显示, 与超像素级视觉特征一起使用的非参数 SCLPLP 取得了顶级的成绩之一。