Optimum Graph Cuts for Pruning Binary Partition Trees of Polarimetric SAR Images

Salembier, Philippe; Foucher, Samuel

doi:10.1109/tgrs.2016.2566581

Cited by 19 publications

(5 citation statements)

References 27 publications

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“…Indeed, the framework of precision recall curves can be applied to any sequence of partitions without any hierarchical constraint between them. However, hierarchies of partitions are rich representations with strong structural properties that are used by many applications beyond their simple horizontal cuts: energies and algorithms for optimal cut segmentation [6], [11], [12], [46], image filtering with hierarchy pruning [5], [14], [48], objects detection [10], [49], interactive segmentation with multiscale region selection [5], [9], [50]. Therefore, our goal is to complete the standard precision-recall curves with other measures in order to capture more information relevant to those applications fields.…”

Section: Proposed Evaluation Methodologymentioning

confidence: 99%

“…In a hierarchy (of partitions), an image is represented as a sequence of coarse to fine partitions satisfying the strong causality principle [3], [4]: i.e., any partition is a refinement of the previous one in the sequence. They have various applications in image processing and analysis: image segmentation [5], [6], [7], [8], [9], [10], [11], [12], occlusion boundary detection [13], image simplification [6], [9], [14], object detection [5], object proposal [10], visual saliency estimation [15]. In particular, they have gained a large popularity in [7] whose hierarchical approach to the general problem of natural image segmentation outperformed state-of-the-art approaches.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Hierarchical Watersheds

Perret¹,

Cousty²,

Guimarães³

et al. 2018

IEEE Trans. on Image Process.

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Abstract-This article aims to understand the practical features of hierarchies of morphological segmentations, namely the quasi-flat zones hierarchy and watershed hierarchies, and to evaluate their potential in the context of natural image analysis. We propose a novel evaluation framework for hierarchies of partitions designed to capture various aspects of those representations: precision of their regions and contours, possibility to extract high quality horizontal cuts and optimal non-horizontal cuts for image segmentation, and ease of finding a set of regions representing a semantic object. This framework is used to assess and to optimize hierarchies with respect to the possible pre-and post-processing steps. We show that, used in conjunction with a state-of-the-art contour detector, watershed hierarchies are competitive with complex state-of-the-art methods for hierarchy construction. In particular, the proposed framework allows us to identify a watershed hierarchy based on a novel extinction value, the number of parent nodes, that outperforms the other hierarchies of morphological segmentations. This coupled with the fact that watershed hierarchies satisfy clear global optimality properties and can be efficiently computed on large data, make them valuable candidates for various computer vision tasks.Index Terms-mathematical morphology, hierarchy of partitions, watershed segmentation, image analysis.

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Section: Proposed Evaluation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Hierarchical Watersheds

Perret¹,

Cousty²,

Guimarães³

et al. 2018

IEEE Trans. on Image Process.

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show abstract

“…In [3,45], the AIRM distance is used in PolSAR/PolInSAR time-series unsupervised classification with a binary partition tree algorithm applied in the space of covariance matrices. Another method, the nearest regularized subspace, is also modified to incorporate the same manifold metric [46].…”

Section: Hermitian Matrices and Riemannian Geometry In Pol-sarmentioning

confidence: 99%

Riemannian Clustering of PolSAR Data Using the Polar Decomposition

Ciuca,

Vasile,

Congedo

et al. 2024

Signal and Image Processing for Remote Sensing

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Copyright

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“…Although nodes in the coarser levels have the relatively higher heterogeneity than nodes in the finer levels, the number of coarser level nodes is less than those at the finer levels. For the purpose of optimal segmentation, dynamic programming (Salembier and Foucher 2016), which is a greedy algorithm starting from the initial superpixels to extract the optimal partition by minimizing the criterion Equation (6). In the experiment, we set = 2 according to the author's recommendation.…”

Section: Solving By Dynamic Programmingmentioning

confidence: 99%

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Wang

Bai

et al. 2018

Geo-spatial Information Science

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c state Key laboratory for information engineering in surveying, mapping and remote sensing (liesmars), Wuhan University, Wuhan, china ABSTRACT Unmanned aerial vehicle (UAV)-based imaging systems have many superiorities compared with other platforms, such as high flexibility and low cost in collecting images, providing wide application prospects. However, the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images, which poses great challenges for subsequent applications. Therefore, an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time. In this work, we proposed a multi-scale hierarchical representation, i.e. binary partition tree, for analyzing large-scale UAV images. More precisely, we first obtained an initial partition of images by an oversegmentation algorithm, i.e. the simple linear iterative clustering. Next, we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships. Moreover, objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure. Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.

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Optimum Graph Cuts for Pruning Binary Partition Trees of Polarimetric SAR Images

Cited by 19 publications

References 27 publications

Evaluation of Hierarchical Watersheds

Evaluation of Hierarchical Watersheds

Riemannian Clustering of PolSAR Data Using the Polar Decomposition

Analysis of large-scale UAV images using a multi-scale hierarchical representation

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