Model-based inexact graph matching on top of CNNs for semantic scene understanding

• URL: http://arxiv.org/abs/2301.07468v2
• Date: Tue, 1 Aug 2023 07:33:15 GMT
• Title: Model-based inexact graph matching on top of CNNs for semantic scene understanding
• Authors: J\'er\'emy Chopin and Jean-Baptiste Fasquel and Harold Mouch\`ere and Rozenn Dahyot and Isabelle Bloch
• Abstract summary: Deep learning pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing module enforcing structural knowledge about the objects of interest to improve segmentation results. Our approach is shown to be resilient to small training datasets that often limit the performance of deep learning methods.
• Score: 6.106023882846558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning based pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing module enforcing structural knowledge about the objects of interest to improve segmentation results provided by deep learning. This module corresponds to a "many-to-one-or-none" inexact graph matching approach, and is formulated as a quadratic assignment problem. Our approach is compared to a CNN-based segmentation (for various CNN backbones) on two public datasets, one for face segmentation from 2D RGB images (FASSEG), and the other for brain segmentation from 3D MRIs (IBSR). Evaluations are performed using two types of structural information (distances and directional relations, , this choice being a hyper-parameter of our generic framework). On FASSEG data, results show that our module improves accuracy of the CNN by about 6.3% (the Hausdorff distance decreases from 22.11 to 20.71). On IBSR data, the improvement is of 51% (the Hausdorff distance decreases from 11.01 to 5.4). In addition, our approach is shown to be resilient to small training datasets that often limit the performance of deep learning methods: the improvement increases as the size of the training dataset decreases.

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