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Xing Z, He Y. Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107905] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Zheng Y, Yang B, Sarem M. Hierarchical Image Segmentation Based on Nonsymmetry and Anti-Packing Pattern Representation Model. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:2408-2421. [PMID: 33493116 DOI: 10.1109/tip.2021.3052359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Image segmentation is the foundation of high-level image analysis and image understanding. How to effectively segment an image into regions that are "meaningful" to the human visual perception and ensure that the segmented regions are consistent at different resolutions is still a very challenging issue. Inspired by the idea of the Nonsymmetry and Anti-packing pattern representation Model in the Lab color space (NAMLab) and the "global-first" invariant perceptual theory, in this paper, we propose a novel framework for hierarchical image segmentation. Firstly, by defining the dissimilarity between two pixels in the Lab color space, we propose an NAMLab-based color image representation approach that is more in line with the human visual perception characteristics and can make the image pixels fast and effectively merge into the NAMLab blocks. Then, by defining the dissimilarity between two NAMLab-based regions and iteratively executing NAMLab-based merging algorithm of adjacent regions into larger ones to progressively generate a segmentation dendrogram, we propose a fast NAMLab-based algorithm for hierarchical image segmentation. Finally, the complexities of our proposed NAMLab-based algorithm for hierarchical image segmentation are analyzed in details. The experimental results presented in this paper show that our proposed algorithm when compared with the state-of-the-art algorithms not only can preserve more details of the object boundaries, but also it can better identify the foreground objects with similar color distributions. Also, our proposed algorithm can be executed much faster and takes up less memory and therefore it is a better algorithm for hierarchical image segmentation.
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Weakly supervised semantic segmentation by iteratively refining optimal segmentation with deep cues guidance. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05669-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Bhandari AK, Singh N, Kumar IV. Lightning search algorithm-based contextually fused multilevel image segmentation. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106243] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Lei T, Jia X, Liu T, Liu S, Meng H, Nandi AK. Adaptive Morphological Reconstruction for Seeded Image Segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 28:5510-5523. [PMID: 31180855 DOI: 10.1109/tip.2019.2920514] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time.
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Cahuina EC, Cousty J, Kenmochi Y, de Albuquerque Araújo A, Cámara-Chávez G, Guimarães SJF. Efficient Algorithms for Hierarchical Graph-Based Segmentation Relying on the Felzenszwalb–Huttenlocher Dissimilarity. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001419400081] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Hierarchical image segmentation provides a region-oriented scale-space, i.e. a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb–Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimarães et al. proposed in 2012 a method for hierarchizing the popular Felzenszwalb–Huttenlocher method, without providing an algorithm to compute the proposed hierarchy. This paper is devoted to providing a series of algorithms to compute the result of this hierarchical graph-based image segmentation method efficiently, based mainly on two ideas: optimal dissimilarity measuring and incremental update of the hierarchical structure. Experiments show that, for an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than 4 h.
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Affiliation(s)
- Edward Cayllahua Cahuina
- Universidade Federal de Minas Gerais, Computer Science Department, 31270-901 Belo Horizonte, Brazil and Université Paris-Est, ESIEE Paris, F-93162 Noisy-le-Grand, France
| | - Jean Cousty
- Université Paris-Est, LIGM (UMR 8049), CNRS, ENPC, ESIEE Paris, UPEM, F-93162 Noisy-le-Grand, France
- Université Paris Descartes, Laboratoire MAP5 (UMR 8145), 12 Rue de l’École de Médecine, 75006 Paris, France
| | - Yukiko Kenmochi
- Université Paris-Est, LIGM (UMR 8049), CNRS, ENPC, ESIEE Paris, UPEM, F-93162 Noisy-le-Grand, France
| | - Arnaldo de Albuquerque Araújo
- Universidade Federal de Minas Gerais, Computer Science Department, 31270-901 Belo Horizonte, Brazil and Université Paris-Est, ESIEE Paris, F-93162 Noisy-le-Grand, France
| | - Guillermo Cámara-Chávez
- Universidade Federal de Ouro Preto, Computer Science Department, 35400-000 Ouro Preto, Brazil
| | - Silvio Jamil F. Guimarães
- PUC Minas — ICEI — Computer Science Department — VIPLAB, 30535-065 Belo Horizonte, Brazil and Université Paris-Est, ESIEE Paris, F-93162 Noisy-le-Grand, France
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Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation. REMOTE SENSING 2017. [DOI: 10.3390/rs9080840] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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