Toward better semantic segmentation by retaining spectral information using matched wavelet pooling.
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| Title: | Toward better semantic segmentation by retaining spectral information using matched wavelet pooling. |
|---|---|
| Authors: | El-Khamy, Said1 (AUTHOR) elkhamy@ieee.org, El-Bana, Shimaa1,2 (AUTHOR) shimaa.elbanaa@aiet.edu.eg, Al-Kabbany, Ahmad3,4 (AUTHOR) alkabbany@ieee.org, Elragal, Hassan1 (AUTHOR) |
| Source: | Neural Computing & Applications. Apr2025, Vol. 37 Issue 10, p7049-7066. 18p. |
| Subjects: | Convolutional neural networks, Architectural models, Artificial intelligence, Image processing, Image registration |
| Abstract: | Pooling operations, such as average pooling, strided convolution, and max pooling, have become fundamental components of convolutional neural networks (CNNs) due to their ability to capture local features, expand receptive fields, and reduce computational costs. However, in the context of semantic segmentation, these pooling techniques can lead to the loss of crucial spatial details that are necessary for accurate pixel-level predictions. To tackle this issue, extensive research has focused on refining deep CNN models through architectural adaptations and novel training methods. Recent studies have demonstrated the importance of pooling layers, exemplified by innovations like the introduction of wavelet pooling. In our study, we highlight the value of incorporating our previously proposed matched wavelet pooling (MWP) into CNNs to enhance semantic segmentation pipelines. The core concept of MWP challenges the notion that including all sub-bands generated from wavelet decomposition consistently improves accuracy. Instead, we advocate for selecting specific sub-bands for the pooling process in each image during both training and testing. This approach introduces sub-band selection protocols customized for image-specific pooling, designed specifically for semantic segmentation CNN architectures, with a particular focus on the UNet and SegNet models. Across three widely used datasets, our proposed MWP- based pipeline, featuring the MWP-UNet architecture, consistently outperforms conventional pooling methods. It achieves a significant average improvement in intersection over union (IoU) of over 25% compared to recent literature. Additionally, our MWP-SegNet model outperformed the standard SegNet by 12.5% mIoU, further demonstrating the effectiveness of our matched wavelet pooling approach across different network architectures. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 183891945 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Toward better semantic segmentation by retaining spectral information using matched wavelet pooling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22El-Khamy%2C+Said%22">El-Khamy, Said</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> elkhamy@ieee.org</i><br /><searchLink fieldCode="AR" term="%22El-Bana%2C+Shimaa%22">El-Bana, Shimaa</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> shimaa.elbanaa@aiet.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Al-Kabbany%2C+Ahmad%22">Al-Kabbany, Ahmad</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> alkabbany@ieee.org</i><br /><searchLink fieldCode="AR" term="%22Elragal%2C+Hassan%22">Elragal, Hassan</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Apr2025, Vol. 37 Issue 10, p7049-7066. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Architectural+models%22">Architectural models</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Pooling operations, such as average pooling, strided convolution, and max pooling, have become fundamental components of convolutional neural networks (CNNs) due to their ability to capture local features, expand receptive fields, and reduce computational costs. However, in the context of semantic segmentation, these pooling techniques can lead to the loss of crucial spatial details that are necessary for accurate pixel-level predictions. To tackle this issue, extensive research has focused on refining deep CNN models through architectural adaptations and novel training methods. Recent studies have demonstrated the importance of pooling layers, exemplified by innovations like the introduction of wavelet pooling. In our study, we highlight the value of incorporating our previously proposed matched wavelet pooling (MWP) into CNNs to enhance semantic segmentation pipelines. The core concept of MWP challenges the notion that including all sub-bands generated from wavelet decomposition consistently improves accuracy. Instead, we advocate for selecting specific sub-bands for the pooling process in each image during both training and testing. This approach introduces sub-band selection protocols customized for image-specific pooling, designed specifically for semantic segmentation CNN architectures, with a particular focus on the UNet and SegNet models. Across three widely used datasets, our proposed MWP- based pipeline, featuring the MWP-UNet architecture, consistently outperforms conventional pooling methods. It achieves a significant average improvement in intersection over union (IoU) of over 25% compared to recent literature. Additionally, our MWP-SegNet model outperformed the standard SegNet by 12.5% mIoU, further demonstrating the effectiveness of our matched wavelet pooling approach across different network architectures. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-025-11008-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 7049 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Architectural models Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Image processing Type: general – SubjectFull: Image registration Type: general Titles: – TitleFull: Toward better semantic segmentation by retaining spectral information using matched wavelet pooling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: El-Khamy, Said – PersonEntity: Name: NameFull: El-Bana, Shimaa – PersonEntity: Name: NameFull: Al-Kabbany, Ahmad – PersonEntity: Name: NameFull: Elragal, Hassan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 10 Titles: – TitleFull: Neural Computing & Applications Type: main |
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