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binary partition tree for semantic object extraction and image segmentation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2007年第3期17卷 378-383页

作者： Lu, Huihai Woods, John C. Ghanbari, Mohammed Univ Essex Dept Elect Syst Engn Colchester CO4 3SQ Essex England

In this work, we demonstrate a systematic way to analyze a binary partition tree representation of natural images for the purposes of archiving and segmentation. Within the tree structure, these problems are transformed into locating prevalent tree branches. With a user interface these points can be found manually by browsing branches. However, tree visualization is difficult due to the high node density. A simpler version of the tree is desired which facilitates subsequent retrieval whilst maintaining as much semantic detail as possible. By studying the evolution of region statistics, our method highlights nodes which represent the boundary between salient detail and provide a set of tree levels from which simplifications and segmentations can be derived. A series of subjective tests are performed to demonstrate the effectiveness of using the simplified trees for object extraction. Segmentation results are compared to ground truths showing semantic content is maintained.

关键词： binary partition tree image segmentation region space analysis semantic object extraction

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binary partition tree construction from multiple features for image segmentation

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PATTERN RECOGNITION 2018年 84卷 237-250页

作者： Randrianasoa, Jimmy Francky Kurtz, Camille Desjardin, Eric Passat, Nicolas Univ Strasbourg CNRS ICube Strasbourg France Univ Reims CReSTIC Reims France Univ Paris 05 LIPADE Paris France

In the context of image analysis, the binary partition tree (BPT) is a classical data structure for the hierarchical modelling of images at different scales. BPTs belong both to the families of graph-based models and morphological hierarchies. They constitute an efficient way to define sets of nested partitions of image support, that further provide knowledge-guided reduced research spaces for optimization-based segmentation procedures. Basically, a BPT is built in a mono-feature way, i.e. for one given image, and one given metric, by merging pairs of connected image regions that are similar in the induced feature space. Our goal is to design a new family of BPTs, dealing with the need to directly manage multiple features within its building process, Then, we propose a generalization of the BPT construction framework, allowing one to embed multiple features. The cornerstone of our approach relies on a collaborative strategy used to establish a consensus between different metrics, thus enabling to obtain a unified hierarchical segmentation space. In particular, this provides alternatives to the complex issue of metric construction from several -possibly non-comparable- features. To reach that goal, we first revisit the BPT construction algorithm to describe it in a graph-based-formalism. Then, we present the structural and algorithmic evolutions and impacts when embedding multiple features in BPT construction. Final experiments illustrate how this multi-feature framework can be used to build BPTs from multiple metrics computed through the (potentially multiple) image content(s). (C) 2018 Elsevier Ltd. All rights reserved.

关键词： binary partition tree Morphological hierarchies Multiple features Graph-based image processing Image segmentation

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binary partition tree AS A HYPERSPECTRAL SEGMENTATION TOOL FOR TROPICAL RAINFORESTS

BINARY PARTITION TREE AS A HYPERSPECTRAL SEGMENTATION TOOL F...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Tochon, Guillaume Feret, Jean-Baptiste Martin, Roberta E. Tupayachi, Raul Chanussot, Jocelyn Asner, Gregory P. Carnegie Inst Sci Dept Global Ecol Stanford CA 94305 USA

ISBN: (纸本)9781467311595

Individual tree crown delineation in tropical forests is of great interest for ecological applications. In this paper we propose a method for hyperspectral image segmentation based on binary tree partitioning. The initial partition is obtained from a watershed transformation in order to make the method computationally more efficient. Then we use a non-parametric region model based on histograms to characterize the regions and the diffusion distance to define the region merging order. The pruning strategy is based on the discontinuity of size increment observed when iteratively merging the regions. The segmentation quality is assessed visually and appears to perform well on most cases, but tree delineation could be improved by including structural information derived from LiDAR data.

关键词： binary partition tree Carnegie airborne observatory hyperspectral imagery segmentation tree crown delineation tropical forest

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binary partition tree AS A POLARIMETRIC SAR DATA REPRESENTATION IN THE SPACE-TIME DOMAIN

BINARY PARTITION TREE AS A POLARIMETRIC SAR DATA REPRESENTAT...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Alonso-Gonzalez, Alberto Lopez-Martinez, Carlos Salembier, Philippe Univ Politecn Cataluna Signal Theory & Commun Dept TSC ES-08034 Barcelona Spain

ISBN: (纸本)9781457710056

The aim of this paper is to present a Polarimetric Synthetic Aperture Radar data processing technique on the space-time domain. This approach is based on a binary partition tree (BPT), which is a region-based and multi-scale data representation. Results with series of RADARSAT-2 real data are analyzed from the point of view of speckle filtering and change detection applications, to illustrate the capabilities to detect and preserve spatial and temporal contours.

关键词： SAR SAR Polarimetry binary partition tree Segmentation Change detection

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Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction

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SIGNAL PROCESSING 2011年第2期91卷 290-299页

作者： Liu, Zhi Shen, Liquan Zhang, Zhaoyang Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China Shanghai Univ Minist Educ Key Lab Adv Display & Syst Applicat Shanghai 200072 Peoples R China

This paper proposes an unsupervised image segmentation approach aimed at salient object extraction. Starting from an over-segmentation result of a color image, region merging is performed using a novel dissimilarity measure considering the impact of color difference, area factor and adjacency degree, and a binary partition tree (BPT) is generated to record the whole merging sequence. Then based on a systematic analysis of the evaluated BPT, an appropriate subset of nodes is selected from the OPT to represent a meaningful segmentation result with a small number of segmented regions. Experimental results demonstrate that the proposed approach can obtain a better segmentation performance from the perspective of salient object extraction. (C) 2010 Elsevier B.V. All rights reserved.

关键词： binary partition tree Image segmentation Region merging Salient object extraction

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Object recognition in hyperspectral images using binary partition tree representation

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PATTERN RECOGNITION LETTERS 2015年 56卷 45-51页

作者： Valero, Silvia Salembier, Philippe Chanussot, Jocelyn Univ Toulouse CESBIO CNES CNRS UMR 5126 IRD Toulouse France Tech Univ Catalonia UPC Barcelona Catalonia Spain Univ Iceland Fac Elect & Comp Engn Reykjavik Iceland Grenoble Inst Technolo4 GIPSA Lab Signal & Image Dept Grenoble France

In this work, an image representation based on binary partition tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Object based image analysis binary partition tree Hyperspectral images Object recognition

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An efficient face segmentation algorithm based on binary partition tree

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2005年第4期20卷 295-314页

作者： Liu, Z Yang, H Peng, NS Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China

This paper presents an efficient face segmentation algorithm based on binary partition tree. Skin-like regions are first obtained by integrating the results of pixel classification and watershed segmentation. Facial features are extracted by the techniques of valley detection and entropic thresholding, and are used to refine the skin-like regions. In order to segment the facial regions from the skin-like regions, a novel region merging algorithm is proposed by considering the impact of the common border ratio between adjacent regions, and the binary partition tree is used to represent the whole region merging process. Then the facial likeness of each node in the binary partition tree is evaluated using a set of fuzzy membership functions devised for a number of facial primitives of geometrical, elliptical and facial features. Finally, an efficient algorithm of node selecting in the binary partition tree is proposed for the final face segmentation, which can exactly segment the faces without any underlying assumption. The performance of the proposed face segmentation algorithm is demonstrated by experimental results carried out on a variety of images in different scenarios. (c) 2005 Elsevier B.V. All rights reserved.

关键词： face segmentation binary partition tree region merging watershed segmentation fuzzy membership function

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On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images

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REMOTE SENSING OF ENVIRONMENT 2015年 159卷 318-331页

作者： Tochon, G. Feret, J. B. Valero, S. Martin, R. E. Knapp, D. E. Salembier, P. Chanussot, J. Asner, G. P. GIPSA Lab F-38400 St Martin Dheres France Carnegie Inst Sci Dept Global Ecol Stanford CA 94305 USA CESBIO F-31400 Toulouse France Tech Univ Catalonia UPC Barcelona 08034 Spain Univ Iceland Fac Elect & Comp Engn Reykjavik Iceland

The segmentation of remotely sensed images acquired over tropical forests is of great interest for numerous ecological applications, such as forest inventories or conservation and management of ecosystems, for which species classification techniques and estimation of the number of individuals are highly valuable inputs. In this paper, we propose a method for hyperspectral image segmentation, based on the binary partition tree (BPT) algorithm, and we apply it to two sites located in Hawaiian and Panamean tropical rainforests. Different strategies combining spatial and spectral dimensionality reduction are compared prior to the construction of the BPT. Various superpixel generation methods including watershed transformation and mean shift clustering are applied to decrease spatial dimensionality and provide an initial segmentation map. Principal component analysis is performed to reduce the spectral dimensionality and different combinations of principal components are compared. A non-parametric region model based on histograms, combined with the diffusion distance to merge regions, is used to build the BPT. An adapted pruning strategy based on the size discontinuity of the merging regions is proposed and compared with an already existing pruning strategy. Finally, a set of criteria to assess the quality of the tree segmentation is introduced. The proposed method correctly segmented up to 68% of the tree crowns and produced reasonable patterns of the segmented landscapes. (C) 2015 Elsevier Inc. All rights reserved.

关键词： binary partition tree Carnegie Airborne Observatory Hyperspectral imagery Segmentation tree crown delineation Tropical forest

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Use of binary partition tree and energy minimization for object-based classification of urban land cover

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2015年第Apr.期102卷 48-61页

作者： Li, Mengmeng Bijker, Wietske Stein, Alfred Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands

Two main challenges are faced when classifying urban land cover from very high resolution satellite images: obtaining an optimal image segmentation and distinguishing buildings from other man-made objects. For optimal segmentation, this work proposes a hierarchical representation of an image by means of a binary partition tree (BPT) and an unsupervised evaluation of image segmentations by energy minimization. For building extraction, we apply fuzzy sets to create a fuzzy landscape of shadows which in turn involves a two-step procedure. The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information. The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level. We conducted the experiments on two datasets of Pleiades images from Wuhan City, China. To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification. The results show that the proposed classification produced the highest overall accuracies and kappa coefficients, and the smallest over-classification and under-classification geometric errors. We conclude first that integrating BPT with energy minimization offers an effective means for image segmentation. Second, we conclude that the directional relationship between building and shadow objects represented by a fuzzy landscape is important for building extraction. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Urban land cover binary partition tree Energy minimization Fuzzy landscapes Directional relationship Segmentation

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Region-based urban road extraction from VHR satellite images using binary partition tree

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年 44卷 217-225页

作者： Li, Mengmeng Stein, Alfred Bijker, Wietske Zhan, Qingming Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands Wuhan Univ Sch Urban Design Wuhan 430072 Peoples R China Wuhan Univ Res Ctr Digital City Wuhan 430072 Peoples R China

This paper provides a hierarchical method for urban road extraction. It consists of (1) obtaining the road region of interest from a VHR image, (2) hierarchically representing this road region of interest in a binary partition tree (BPT), and extracting the roads based on this BPT at hierarchical levels. Besides using two existing geometrical features (i.e. compactness and elongation), we define two other structural features based on orientation histograms and morphological profiles to guide the region merging of BPT. The morphological profiles are constructed using a series of path openings, which facilitate modeling linear or curved structures. The proposed method was applied to two types of VHR images with different urban settings, corresponding to a Pleiades-B image of Wuhan, China, and a Quickbird image of Enschede, the Netherlands. Experimental results show that the proposed method was able to group adjacent small segments that have high spectral heterogeneity and low road-like geometrical properties to form more meaningful roads sections, and performed superior to the existing methods. Furthermore, we compared the proposed method with two other existing methods in the literature. We conclude that the proposed method can provide an effective means for extracting roads over densely populated urban areas from VHR satellite images. (C) 2015 Elsevier B.V. All rights reserved.

关键词： binary partition tree Fuzzy aggregation Morphological profiles Road extraction Very high resolution images

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