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hierarchical image segmentation Based on Iterative Contraction and Merging

IEEE TRANSACTIONS ON image PROCESSING 2017年第5期26卷 2246-2260页

作者： Syu, Jia-Hao Wang, Sheng-Jyh Wang, Li-Chun Natl Chiao Tung Univ Commun Engn Dept Hsinchu 30010 Taiwan Natl Chiao Tung Univ Elect Engn Dept Hsinchu 30010 Taiwan

In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.

关键词： Affinity matrix contraction process hierarchical image segmentation

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hierarchical image segmentation based on similarity of NDVI time series

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REMOTE SENSING OF ENVIRONMENT 2008年第2期112卷 506-521页

作者： Lhermitte, Stefaan Verbesselt, Jan Jonckheere, Inge Nackaerts, Kris van Aardt, Jan A. N. Verstraeten, Willem W. Coppin, Pol Katholieke Univ Leuven Biosyst Dept M3 BIORES Louvain Belgium Integraph Belgium BV Brussels Belgium CSIR Pretoria South Africa

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover-landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes. (C) 2007 Elsevier Inc. All rights reserved.

关键词： hierarchical image segmentation fast Fourier transform NDVI time series vegetation mapping

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hierarchical image segmentation BY MULTIDIMENSIONAL CLUSTERING AND ORIENTATION-ADAPTIVE BOUNDARY REFINEMENT

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PATTERN RECOGNITION 1995年第5期28卷 695-709页

作者： SCHROETER, P BIGUN, J SWISS FED INST TECHNOL DEPT ELECT ENGN CH-1015 LAUSANNE SWITZERLAND

In this paper we present a new multi-dimensional segmentation algorithm. We propose an orientation-adaptive boundary estimation process, embedded in a multiresolution pyramidal structure, that allows the use of different clustering procedures without spatial connectivity constraints. The presence of noise in the feature space, mainly produced by modeling errors, causes a class-overlap which can be reduced in a multiresolution pyramid. At the coarsest resolution level, the separation between the different classes is increased and the within-class variance reduced. Thus, at this level, the classes can be obtained with different multi-dimensional clustering algorithms without connectivity constraints. Small and scattered classes as well as isolated class labels are reassigned to their neighborhood by a process which guarantees the spatial connectivity. The resolution is then increased by projecting down the class labels. At each level, the borders are improved by reassigning the boundary pixels to their spatially closest class. However, the class-uncertainty astride the borders has first to be reduced, and we propose to do this by means of orientation-adaptive butterfly-shaped filters. This refinement process further eliminates spatially misclassified pixels produced by the unconstrained clustering. Experimental results show that similarly accurate boundaries are obtained with different clustering algorithms for various test images.

关键词： hierarchical image segmentation TEXTURE segmentation PYRAMIDS MULTIDIMENSIONAL CLUSTERING DETERMINISTIC RELAXATION ORIENTATION-ADAPTIVE FILTERS BOUNDARY REFINEMENT

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hierarchical image segmentation Relying on a Likelihood Ratio Test 18th

Hierarchical Image Segmentation Relying on a Likelihood Rati...

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18th International Conference on image Analysis and Processing (ICIAP)

作者： Guimaraes, Silvio Jamil F. do Patrocinio, Zenilton Kleber G., Jr. Kenmochi, Yukiko Cousty, Jean Najman, Laurent PUC Minas ICEI DCC VIPLAB Belo Horizonte MG Brazil Univ Paris Est LIGM ESIEE Paris CNRS Champs Sur Marne France

ISBN: (纸本)9783319232348;9783319232331

hierarchical image segmentation provides a set of image segmentations at different detail levels in which coarser details levels can be produced by simple merges of regions from segmentations at finer detail levels. However, many image segmentation algorithms relying on similarity measures lead to no hierarchy. One of interesting similarity measures is a likelihood ratio, in which each region is modelled by a Gaussian distribution to approximate the cue distributions. In this work, we propose a hierarchical graph-based image segmentation inspired by this likelihood ratio test. Furthermore, we study how the inclusion of hierarchical property have influenced the computation of quality measures in the original method. Quantitative and qualitative assessments of the method on three well known image databases show efficiency.

关键词： hierarchical image segmentation Graph-based method Statistical properties

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Multi-scale region composition of hierarchical image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32833-32855页

作者： Peng, Bo Al-Huda, Zaid Xie, Zhuyang Wu, Xi Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Natl Engn Lab Integrated Transportat Big Data App Chengdu Peoples R China Chengdu Univ Informat Technol Sch Comp Sci Chengdu 610225 Peoples R China

hierarchical image segmentation is a prominent trend in the literature as a way to improve the segmentation quality. Generally, meaningful objects in an image are described by segments from different scales. Thus, one may spend extra effort on searching for the best representation of objects in the hierarchical segmentation result. In this paper, a novel algorithm is proposed to optimally select the segmentation scale, which leads to a composite segmentation as the output. To this end, the quality of regions from different scales of the hierarchical segmentation is evaluated. Then, a graphical model is constructed as a set of nodes. The weights among nodes are computed according to the segmentation quality of regions in multiple levels. In order to optimize the labeling of each node in the graph, the composition process is performed twice with two sampling intervals. Comprehensive experiments are conducted on different datasets for popular hierarchical image segmentation algorithms. The results show that the output of the proposed algorithm can improve the quality of hierarchical segmentation in a single scale at a low cost of computation.

关键词： hierarchical image segmentation segmentation evaluation Graphical model Scale selection

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Re-ranking of the Merging Order for hierarchical image segmentation 20th

Re-ranking of the Merging Order for Hierarchical Image Segme...

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20th Iberoamerican Congress on Pattern Recognition (CIARP)

作者： do Patrocinio, Zenilton Kleber G., Jr. Guimaraes, Silvio Jamil F. Pontificia Univ Catolica Minas Gerais Belo Horizonte MG Brazil

ISBN: (纸本)9783319257518;9783319257501

hierarchical image segmentation provides a set of image segmentations at different detail levels in which coarser detail levels can be produced from merges of regions belonging to finer detail levels. However, similarity measures adopted by hierarchical image segmentation methods do not always consider the homogeneity of the combined components. In this work, we propose a hierarchical graph-based image segmentation using a new similarity measure based on the variability of the merged components which is responsible for the re-ranking of the merging order that was originally established by the minimum spanning tree. Furthermore, we study how the inclusion of this characteristic has influenced the quality measures. Experiments have shown the superior performance of the proposed method on three well known image databases, and its robustness to noise was also demonstrated.

关键词： hierarchical image segmentation Graph-based method Similarity measure Region-merging criterion

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Evaluation of Scale-Aware Realignments of hierarchical image segmentation 23rd

Evaluation of Scale-Aware Realignments of Hierarchical Image...

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23rd Iberoamerican Congress on Pattern Recognition (CIARP)

作者： Adao, Milena M. Ferzoli Guimaraes, Silvio Jamil Patrocinio, Zenilton K. G., Jr. Pontificia Univ Catolica Minas Gerais Comp Sci Dept Belo Horizonte MG Brazil

ISBN: (纸本)9783030134686;9783030134693

A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects may appear at different scales due to their size or to their distance from the camera. One possible solution to cope with that is to realign the hierarchy such that every region containing an object is at the same level (or scale). In this work, we explore the use of regression to predict the best scale value for given region, which is then used to realign the entire hierarchy. Experimental results are presented for two different segmentation methods;along with an analysis of the adoption of different combination of mid-level features to describe regions.

关键词： hierarchical image segmentation Alignment of hierarchy Regression Random forest Neural network

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Optimal Scale of hierarchical image segmentation with Scribbles Guidance for Weakly Supervised Semantic segmentation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2021年第10期35卷

作者： Al-Huda, Zaid Zhai, Donghai Yang, Yan Algburi, Riyadh Nazar Ali Southwest Jiaotong Univ Sch Comp & Artificial Intelligent Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is 69.3% mIoU.

关键词： Deep convolutional neural networks graphical model hierarchical image segmentation semantic segmentation weakly supervised segmentation

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HWT - Hybrid watershed transform: Optimal combination of hierarchical interactive and automated image segmentation

HWT - Hybrid watershed transform: Optimal combination of hie...

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Medical Imaging 2007 Conference

作者： Hahn, Horst K. Wenzel, Markus T. Drexl, Johann Zentis, Susanne Peitgen, Heinz-Otto MeVis Res Ctr Med Image Comp Univ Allee 29 D-28359 Bremen Germany

ISBN: (纸本)9780819466303

In quantitative medical imaging and therapy planning, the optimal combination of automated and interactively defined information is crucial for image segmentation methods to be both efficient and effective. We propose to combine an efficient hierarchical region merging scheme that collects per-region statistics across hierarchy levels with a trainable classification engine that facilitates automated region labeling based on an arbitrary number of reference segmentations. When applying the classification engine, we propose to use a corridor of non-classified regions resulting in a sparse labeling with extremely low false-classification rate, and to attribute labels to the remaining basins through successive merging with ready-labeled basins. The proposed hierarchical region merging scheme also permits to efficiently include interactively defined labels. We denominate this general approach as Hybrid hierarchical Interactive image segmentation Scheme (HIS2). More specifically, we present an extension of the Interactive Watershed Transform, which we combine with a trainable two-class Support Vector Machine based on Gaussian radial basis functions. Finally, we present a novel asymmetric marker scheme, which provides a powerful means of regionally correcting remaining inaccuracies while preserving full detail of the automatic labeling procedure. We denominate the complete algorithm as Hybrid Watershed Transform (HWT), which we apply to one challenging segmentation problem in clinical imaging, namely efficient bone removal in large computed tomography angiographic data sets. Efficiency and accuracy of the proposed methodology is evaluated on multi-slice images from nine different sites. As a result, its ability to rapidly and automatically generate robust and precise segmentation results in combination with a versatile manual correction mechanism could be proven without requiring specific anatomical or geometrical models.

关键词： medical image analysis hierarchical image segmentation watershed transform automation efficient human-computer interaction CT angiography bone removal

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Fuzzy image segmentation based on the hierarchical Divide and Link clustering algorithm 10

Fuzzy Image Segmentation based on the hierarchical Divide an...

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10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： Guada, Carely Gomez, Daniel Tinguaro Rodriguez, J. Yanez, Javier Montero, Javier Univ Complutense Fac Ciencias Matemat E-28040 Madrid Spain Univ Complutense Fac Estudios Estadist E-28040 Madrid Spain Univ Complutense Fac Ciencias Matemat Inst IGEO UCM CSIC UCM E-28040 Madrid Spain

ISBN: (纸本)9781467393232

In this paper, we present a method to obtain a Fuzzy image segmentation from the hierarchical clustering algorithm Divide and Link. The Divide and Link algorithm consists on treating a digital image as a graph, then building spanning forest through a Kruskal scheme to successively sort the edges while partitions are obtained. This process is driven until all the pixels of the image are segmented, that is, there are as many regions as pixels.

关键词： Fuzzy image segmentation hierarchical image segmentation image network spanning forest clustering

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