检索结果-内蒙古大学图书馆

image segmentation algorithms based on the machine learning of features

PATTERN RECOGNITION LETTERS 2010年第14期31卷 2325-2336页

作者： Lee, Sang Hak Koo, Hyung Il Cho, Nam Ik Seoul Natl Univ Sch Elect Engn Seoul South Korea

For general purpose image segmentation, it is required to find and integrate the features that best characterize the regions to be segmented. This paper proposes a machine learning approach to finding the appropriate features and also a new segmentation method based on the information obtained while learning. Precisely, our method is based on the AdaBoost algorithm for learning the difference between regions, and the CRF-based (conditional random fields) energy formulation for the segmentation using the information from the learning. We have applied our method to interactive (semi-automatic) and unsupervised (fully-automatic) segmentation problems. While the interactive case is relatively straightforward due to the nature of our machine learning scheme, the unsupervised case is not. Hence, for the unsupervised segmentation, we devise a new initialization method and an EM-like (Expectation-Maximization) optimization method that iterates AdaBoost learning and graph-cuts. The analysis shows that the number of regions is automatically determined so that only distinguishable regions are survived. Experimental results also show that the proposed method gives promising results in diverse applications such as texture segmentation, color-texture segmentation, and page segmentation. 2010 Elsevier B.V. All rights reserved.

关键词： image segmentation Interactive image segmentation unsupervised image segmentation AdaBoost image features

来源：评论

学校读者我要写书评

暂无评论

Automatic image segmentation using salient key point extraction and star shape prior

引用

SIGNAL PROCESSING 2014年 105卷 122-136页

作者： Liao, Xiangli Xu, Hongbo Zhou, Yicong Li, Kunqian Tao, Wenbing Guo, Qiuju Liu, Liman Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Cent China Normal Univ Coll Phys Sci & Technol Wuhan 430074 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China South Cent Univ Nationalities Sch Biomed Engn Wuhan 430074 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China

In this paper, a new unsupervised segmentation method is proposed. The method integrates the star shape prior of the image object with salient point detection algorithm. In the proposed method, the Harris salient point detection is first applied to the color image to obtain the initial salient points. A regional contrast based saliency extraction method is then used to select rough object regions in the image. To restrict the distribution of salient points, an adaptive threshold segmentation is applied to the saliency map to get the saliency mask. And then the salient region points can be obtained by placing the saliency mask on the initial Harris salient points. In order to make sure the salient points which we get are inside the image object thus the star shape constraint can be applied to the graph cuts segmentation, the Affinity Propagation (AP) clustering is employed to find the salient key points among the salient region points. Finally, these salient key points are regarded as foreground seeds and the star shape prior is introduced to graph cuts segmentation framework to extract the foreground object. Extensive experiments and comparisons on public database are provided to demonstrate the good performance of the proposed method. (c) 2014 Elsevier B.V. All rights reserved.

关键词： Graph cut Salient point Star shape prior Affinity propagation cluster unsupervised image segmentation

来源：评论

学校读者我要写书评

暂无评论

MDS-Based Multiresolution Nonlinear Dimensionality Reduction Model for Color image segmentation

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第3期22卷 447-460页

作者： Mignotte, Max Univ Montreal Dept Informat & Rech Operat Fac Arts & Sci Montreal PQ H3C 3J7 Canada

In this paper, we present an efficient coarse-to-fine multiresolution framework for multidimensional scaling and demonstrate its performance on a large-scale nonlinear dimensionality reduction and embedding problem in a texture feature extraction step for the unsupervised image segmentation problem. We demonstrate both the efficiency of our multiresolution algorithm and its real interest to learn a nonlinear low-dimensional representation of the texture feature set of an image which can then subsequently be exploited in a simple clustering-based segmentation algorithm. The resulting segmentation procedure has been successfully applied on the Berkeley image database, demonstrating its efficiency compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.

关键词： Berkeley image database color textured image multidimensional scaling multiresolution optimization nonlinear dimensionality reduction probability rand index unsupervised image segmentation

来源：评论

学校读者我要写书评

暂无评论

image segmentation by iterative optimization of multiphase multiple piecewise constant model and Four-Color relabeling

引用

PATTERN RECOGNITION 2011年第12期44卷 2819-2833页

作者： Liu, Liman Tao, Wenbing Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intellegence Wuhan 430074 Peoples R China

In the paper an iteratively unsupervised image segmentation algorithm is developed, which is based on our proposed multiphase multiple piecewise constant (MMPC) model and its graph cuts optimization. The MMPC model use multiple constants to model each phase instead of one single constant used in Chan and Vese (CV) model and cartoon limit so that heterogeneous image object segmentation can be effectively dealt with. We show that the multiphase optimization problem based on our proposed model can be approximately solved by graph cuts methods. Four-Color theorem is used to relabel the regions of image after every iteration, which makes it possible to represent and segment an arbitrary number of regions in image with only four phases. Therefore, the computational cost and memory usage are greatly reduced. The comparison with some typical unsupervised image segmentation methods using a large number of images from the Berkeley segmentation Dataset demonstrates the proposed algorithm can effectively segment natural images with a good performance and acceptable computational time. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： unsupervised image segmentation Multiphase optimization Four-Color relabeling Iterative algorithm Mean shift

来源：评论

学校读者我要写书评

暂无评论

Color image segmentation and parameter estimation in a markovian framework

引用

PATTERN RECOGNITION LETTERS 2001年第3-4期22卷 309-321页

作者： Kato, Z Pong, TC Lee, JCM Hong Kong Univ Sci & Technol Dept Comp Sci Kowloon Hong Kong Peoples R China Natl Univ Singapore Sch Comp Singapore 117543 Singapore

An unsupervised color image segmentation algorithm is presented, using a Markov random field (MRF) pixel classification model. We propose a new method to estimate initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： unsupervised image segmentation color Markov random fields pixel classification parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Extension of higher-order HMC modeling with application to image segmentation

引用

DIGITAL SIGNAL PROCESSING 2008年第5期18卷 849-860页

作者： Benyoussef, Lamia Carincotte, Cyril Derrode, Stephane Ecole Cent Marseille CNRS Inst Fresnel GSM GrpUMR 6133 F-13451 Marseille 20 France Multitel Asbl B-7000 Mons Belgium

In this work, we propose to improve the neighboring relationship ability of the hidden Markov chain (HMC) model, by extending the memory lengths of both the Markov chain process and the data-driven densities arising in the model. The new model is able to learn more complex noise structures, with respect to the configuration of several previous states and observations. Model parameters estimation is performed from an extension of the general iterative conditional estimation (ICE) method to take into account memories, which makes the classification algorithm unsupervised. The higher-order HMC model is then evaluated in the image segmentation context. A comparative study conducted on a simulated image is carried out according to the order of the chain. Experimental results on a synthetic aperture radar (SAR) image show that higher-order model can provide more homogeneous segmentations than the classical model, but to the cost of higher memory and computing time requirements. (c) 2007 Elsevier Inc. All rights reserved.

关键词： unsupervised image segmentation higher-order hidden Markov chain iterative conditional estimation maximal posterior mode

来源：评论

学校读者我要写书评

暂无评论

DATA-DRIVEN TREE-STRUCTURED BAYESIAN NETWORK FOR image segmentation

DATA-DRIVEN TREE-STRUCTURED BAYESIAN NETWORK FOR IMAGE SEGME...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kampa, Kittipat (Bot) Principe, Jose C. Putthividhya, Duangmanee (Pew) Rangarajan, Anand Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA EBay Inc San Jose CA 95125 USA

ISBN: (纸本)9781467300469

This paper presents Data-Driven Tree-structured Bayesian network (DDT), a novel probabilistic graphical model for hierarchical unsupervised image segmentation. The DDT captures long and short-ranged correlations between neighboring regions in each image using a tree-structured prior. Unlike other previous work, DDT first segments an input image into superpixels and learn a tree-structured prior based on the topology of superpixels in different scales. Such a tree structure is referred to as data-driven tree structure. Each superpixel is represented by a variable node taking a discrete value of class/label of the segmentation. The probabilistic relationships among the nodes are represented by edges in the network. The unsupervised image segmentation, hence, can be viewed as an inference problem of the nodes in the tree structure of DDT, which can be carried out efficiently. We evaluate quantitatively our results with respect to the ground-truth segmentation, demonstrating that our proposed framework performs competitively with the state of the art in unsupervised image segmentation and contour detection.

关键词： unsupervised image segmentation tree structure Bayesian networks graphical models superpixels

来源：评论

学校读者我要写书评

暂无评论

IRREGULAR TREE-STRUCTURED BAYESIAN NETWORK FOR image segmentation

IRREGULAR TREE-STRUCTURED BAYESIAN NETWORK FOR IMAGE SEGMENT...

引用

21st IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Kampa, Kittipat (Bot) Putthividhya, Duangmanee (Pew) Principe, Jose C. Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA eBay Inc San Jose CA 95125 USA

ISBN: (纸本)9781457716232

unsupervised image segmentation algorithms rely heavily on a probabilistic smoothing prior to enforce local homogeneity in the segmentation results. The tree-structured prior [1, 2, 3] is one such prior which allows important multi-scale spatial correlations that exist in natural images to be captured. Two main types of tree structure prior have been previously proposed: 1) fixed quadtree structure [1], which suffers from "blockiness" in the segmentation results and 2) flexible tree structure [2, 3] which can adapt its structure to the natural object boundary but at a significant computational cost. This paper presents a novel probabilistic unsupervised image segmentation framework called Irregular Tree-Structured Bayesian Networks (ITSBN) which introduces the notion of irregular tree structure that combines the merits of the two previous approaches. As in [2, 3], more natural object boundaries can be modeled in our framework since a tree is learned for each input image. Our method, however, does not update the adaptive structure at every iteration which drastically reduces the computation required. We derive a time-efficient exact inference algorithm based on a sum-product framework using factor graphs [4]. Furthermore, a novel methodology for the evaluation of unsupervised image segmentation is proposed. By integrating non-parametric density estimation techniques with the traditional precision-recall framework, the proposed method is more robust to boundary inconsistency due to human subjects.

关键词： unsupervised image segmentation tree structure Bayesian networks graphical models precision-recall framework

来源：评论

学校读者我要写书评

暂无评论

Fast image segmentation with Contextual Scan and Markov Chains 29

Fast Image Segmentation with Contextual Scan and Markov Chai...

引用

29th European Signal Processing Conference (EUSIPCO)

作者： Fernandes, Clement Monti, Thomas Monfrini, Emmanuel Pieczynski, Wojciech Segula Matra Automot Dept Elect & Syst Trappes France Inst Polytech Paris Dept CITI Telecom Sudparis Evry France

ISBN: (纸本)9789082797060

Transforming bi-dimensional images into mono-dimensional sequences with Peano scan (PS) allows using Hidden Markov Chains (HMCs) for unsupervised image segmentation. In some situations, such methods can be competitive compared to Hidden Markov Fields (HMFs) based ones, while being much faster. We propose enriching the HMC-PS model by introducing "contextual" Peano scan (CPS). It consists in associating to each index in the HMC obtained from PS, two observations on pixels which are neighbors of the pixel considered in the image, but are not its neighbors in the HMC. This gives three observations on each point of the Peano scan, which leads to a new HMC with a more complex structure, but whose prior and posterior laws are still Markovian. Therefore we can apply the usual parameter estimation method: Stochastic Expectation-Maximization (SEM), as well as study unsupervised segmentation Marginal Posterior Mode (MPM) so obtained. The CPS based supervised and unsupervised MPM are compared to the classic scan based HMC-PS and the HMF through experiments on artificial images. They improve notably the former, and can even compete with the latter.

关键词： hidden Markov chains Peano scan unsupervised image segmentation contextual Peano scan SEM

来源：评论

学校读者我要写书评

暂无评论

unsupervised leaf segmentation in complex backgrounds using mutual information minimization

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期45卷 10127-10139页

作者： Guo, Yijian Sun, Kaiqiong Luo, Gang Wang, Meng Wuhan Polytech Univ Sch Math & Comp Sci Wuhan 430023 Peoples R China

Leaf segmentation is crucial for plant recognition, especially for tree species identification. In natural environments, leaf segmentation can be very challenging due to the lack of prior information about leaves and the variability of backgrounds. In typical applications, supervised algorithms often require pixel-level annotation of regions, which can be labour-intensive and limited to identifying plant species using pre-labelled samples. On the other hand, traditional unsupervised image segmentation algorithms require specialised parameter tuning for leaf images to achieve optimal results. Therefore, this paper proposes an unsupervised leaf segmentation method that combines mutual information with neural networks to better generalise to unknown samples and adapt to variations in leaf shape and appearance to distinguish and identify different tree species. First, a model combining a Variational Autoencoder (VAE) and a segmentation network is used as a pre-segmenter to obtain dynamic masks. Secondly, the dynamic masks are combined with the segmentation masks generated by the mask generator module to construct the initial mask. Then, the patcher module uses the Mutual Information Minimum (MIM) loss as an optimisation objective to reconstruct independent regions based on this initial mask. The process of obtaining dynamic masks through pre-segmentation is unsupervised, and the entire experimental process does not involve any label information. The experimental method was performed on tree leaf images with a naturally complex background using the publicly available Pl@ntLeaves dataset. The results of the experiment showed that compared to existing excellent methods on this dataset, the IoU (Intersection over Union) index increased by 3.9%.

关键词： Leaf extraction deep learning unsupervised image segmentation mutual information VAE

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：