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Exemplar-Based Recursive Instance Segmentation With Application to Plant Image Analysis
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DOI：10.1109/TIP.2019.2923571.png)
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摘要
En 中文
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.
Keyword:
Image segmentation
Probabilistic logic
Object detection
Optimization
Shape
Computer vision
Computational modeling
Instance segmentation
exemplar-based
Hough voting
plant leaf segmentation
plant phenotyping
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期刊
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13.7
论文数:
1.0W
被引数:
8.4W
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