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Exemplar-Based Recursive Instance Segmentation With Application to Plant Image Analysis

机译:基于示例基于的递归实例分段,应用于植物图像分析

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摘要

Instance segmentation is a challenging computer vision problem which lies at the intersection of object detection and semantic segmentation. Motivated by plant image analysis in the context of plant phenotyping, a recently emerging application field of computer vision, this paper presents the exemplar-based recursive instance segmentation (ERIS) framework. A three-layer probabilistic model is first introduced to jointly represent hypotheses, voting elements, instance labels, and their connections. Afterward, a recursive optimization algorithm is developed to infer the maximum a posteriori (MAP) solution, which handles one instance at a time by alternating among the three steps of detection, segmentation, and update. The proposed ERIS framework departs from previous works mainly in two respects. First, it is exemplar-based and model-free, which can achieve instance-level segmentation of a specific object class given only a handful of (typically less than 10) annotated exemplars. Such a merit enables its use in case that no massive manually-labeled data is available for training strong classification models, as required by most existing methods. Second, instead of attempting to infer the solution in a single shot, which suffers from extremely high computational complexity, our recursive optimization strategy allows for reasonably efficient MAP-inference in full hypothesis space. The ERIS framework is substantialized for the specific application of plant leaf segmentation in this work. Experiments are conducted on public benchmarks to demonstrate the superiority of our method in both effectiveness and efficiency in comparison with the state-of-the-art.
机译:实例分割是一个具有挑战性的计算机视觉问题,其位于物体检测和语义分割的交叉点。通过植物图像分析在植物表型上下文中的动机,最近新兴的计算机视野,本文提出了基于示例的递归实例分段(ERIS)框架。首先引入三层概率模型以共同代表假设,投票元素,实例标签及其连接。之后,开发了一种递归优化算法以推断出最大的后验(MAP)解决方案,该解决方案一次通过交替在检测,分段和更新的三个步骤中交替处理一个实例。拟议的ERIS框架主要在两方面离开以前的作品。首先,它是基于示例和无模型的,其可以仅在仅给予特定对象类的实例级分割,仅给予少数(通常小于10)注释的示例。如大多数现有方法所要求的,这种优点使其在不可用于培训强大的分类模型的情况下的使用。其次,而不是尝试在遭受极高计算复杂性的单一拍摄中推断解决方案,而是我们的递归优化策略允许在完全假设空间中具有合理有效的映射推理。 ERIS框架对于本工作中的植物叶细分的特定应用是大小的。在公共基准上进行实验,以展示与最先进的有效性和效率的方法的优势。

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