Instance segmentation with the number of clusters incorporated in embedding learning

• URL: http://arxiv.org/abs/2103.14869v1
• Date: Sat, 27 Mar 2021 10:03:19 GMT
• Title: Instance segmentation with the number of clusters incorporated in embedding learning
• Authors: Jianfeng Cao and Hong Yan
• Abstract summary: We propose to embed clustering information into an embedding learning framework FCRNet. FCRNet relieves the complexity of post process by incorporating the number of clustering groups into the embedding space. The superior performance of FCRNet is verified and compared with other methods on the nucleus dataset BBBC006.
• Score: 15.054120734581826
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic and instance segmentation algorithms are two general yet distinct image segmentation solutions powered by Convolution Neural Network. While semantic segmentation benefits extensively from the end-to-end training strategy, instance segmentation is frequently framed as a multi-stage task, supported by learning-based discrimination and post-process clustering. Independent optimizations on substages instigate the accumulation of segmentation errors. In this work, we propose to embed prior clustering information into an embedding learning framework FCRNet, stimulating the one-stage instance segmentation. FCRNet relieves the complexity of post process by incorporating the number of clustering groups into the embedding space. The superior performance of FCRNet is verified and compared with other methods on the nucleus dataset BBBC006.

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