Learning deep morphological networks with neural architecture search. (November 2022)

• Record Type: Journal Article
• Authors: Hu, Yufei; Belkhir, Nacim; Angulo, Jesus; Yao, Angela; Franchi, Gianni
• Is Part Of: Pattern recognition. Volume 131(2022)
• Publication Date: 2022-11
• Subjects: Mathematical morphology -- Deep learning -- Architecture search -- Edge detection -- Semantic segmentation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108893
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: First, we propose novel procedures based on sub-pixel convolutions and mathematical morphology to construct pseudo-morphological operations using standard convolution layers. We integrate these procedures into deep networks using morphological layers and NAS algorithms. We demonstrate empirically that our architecture tailored to morphological layers can outperform conventional convolutional layers. We outline some current issues in NAS and introduce the problem of choosing the backbone, i.e .the higher-level architecture design on which the search will be performed. We offer novel network space descriptions suitable for the edge identification job. We are the first to examine architectural search mixed with morphological procedures for edge detection. Our new specialized architecture achieves state-of-the-art performance for edge detection. Abstract: Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. The combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. Most non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for various image processing problems. This paper investigates the utility of integrating these operations into an end-to-end deep learning framework. DNNs are designed to acquire a realistic representation for a particular job. Morphological operators give topological descriptors that convey salient information about the shapes of objects depicted in images. We propose a method based on meta-learning to incorporate morphological operators into DNNs. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks, including picture classification, edge detection, and semantic segmentation. Our codes are available at https://nao-morpho.github.io/ .