论文标题
现实世界中场景解析的非参数限制的本地限制
Non-parametric spatially constrained local prior for scene parsing on real-world data
论文作者
论文摘要
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，从不受约束的现实世界数据中解析的准确场景仍然是一项具有挑战性的任务。在本文中，我们介绍了非参数限制的本地先验（SCLP），以在现实数据上解析场景。对于给定的查询图像，通过首先将大多数相似训练图像的子集获取到查询图像，然后从空间图像块之间以及从检索到的子集中收到的相邻超类之间收集有关对象共发生统计的先前信息，从而学习了非参数SCLP。 SCLP在捕获有关查询映像中的对象间相关性的长距离和短距离上下文方面具有强大的功能，并且可以有效地与传统的视觉特征集成以完善分类结果。我们对SIFT流量和Pascal-Context基准数据集进行的实验表明，与最先进的方法相比，与Superpixel级的视觉特征结合使用的非参数SCLP达到了最重要的性能之一。
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.
