检索结果-内蒙古大学图书馆

Conference on Storage and Retrieval for Media Databases 2001

作者： Duygulu, P Yarman-Vural, F Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

ISBN: (纸本)0819439932

Due to the increasing demand and offer of tile technology, the next generation of the image file formats will be more likely to store and retrieve images based oil their semantic content. Thus, an image should be segmented into "meaningful' regions, each of which corresponds to all object and/or background. In this study., we propose a scheme for multi-level image segmentation, based oil a simple descriptor, called "the closest color in the same neighborhood". Tile proposed scheme generates a stack of images without using arty segmentation threshold. The stack of images is hierarchically ordered in a uniformity tree. The uniformity tree is, then, associated with a semantic tree, which is built by tile user for content based representation. The experiments indicate superior results for retrieving images, which consist of fen objects and a background.

关键词： content based image retrieval multi-level image segmentation color descriptor

来源：评论

学校读者我要写书评

暂无评论

multi-level image segmentation Based on Fuzzy - Tsallis Entropy and Differential Evolution

Multi-Level Image Segmentation Based on Fuzzy - Tsallis Entr...

引用

IEEE International Conference on Fuzzy Systems (FUZZ)

作者： Sarkar, S. Das, S. Paul, S. Polley, S. Burman, R. Chaudhuri, S. S. RCCIIT ECE Dept Kolkata 700015 India Indian Stat Inst ECS Unit Kolkata 700108 India Jadavpur Univ ETCE Dept Kolkata 700032 India

ISBN: (纸本)9781479900206

This paper presents a fuzzy partition and Tsallis entropy based thresholding approach for multi-level image segmentation. image segmentation is considered as one of the most critical tasks in image processing and pattern recognition area. However, discriminating many objects present in an image automatically is the most challenging one. As a result, multilevel thresholding based methods gain importance in recent times, because of its ability to split the image into more than one segments. Efficiency of these algorithms still remains a matter of concern. Over the years, fuzzy partition of 1-D histogram has been employed successfully in bi-level image segmentation to improve the separation between object and the background. Here a fuzzy based technique is adopted in multi-level image segmentation scenario using Tsallis entropy based thresholding. Differential Evolution, a widely used meta-heuristic in recent times, is used for lesser computation time of the proposed algorithm. Both visual and statistical comparison of outcomes between Tsallis and Fuzzy - Tsallis entropy based methods are given in this paper to establish the superiority of the technique.

关键词： multi-level image segmentation multi-level Thresholding Tsallis Entropy Fuzzy Entropy Differential Evolution MSSIM

来源：评论

学校读者我要写书评

暂无评论

An improved bat algorithm and its application in multi-level image segmentation

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期37卷 1399-1413页

作者： Yue, Xiaofeng Zhang, Hongbo Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Jilin Peoples R China

As one of the most popular image segmentation techniques, multi-level thresholding is widely used. Much work has been done to improve the efficiency of multi-level thresholding, but satisfied affect is hard to achieve. In this paper, a multi-level image segmentation method using between-class variance (Otsu) based on improved bat algorithm (DWBA) with dynamically adjusting inertia weight and velocity stratification theory is proposed. DWBA algorithm has strong global search ability at the beginning. Then, the local search capability is enhanced with numbers increasing of iterations. More importantly, the performance of DWBA further improved, because bats with different fitness values have diverse velocities. Furthermore, an improved local search strategy is proposed to avoid the current best solution being replaced during iterations. The experimental results established that the proposed DWBA algorithm obtains better outcome than other algorithms.

关键词： multi-level image segmentation DWBA algorithm dynamically adjusting inertia weight velocity stratification theory Otsu

来源：评论

学校读者我要写书评

暂无评论

multi-level image segmentation Based on Fuzzy - Tsallis Entropy and Differential Evolution

Multi-Level Image Segmentation Based on Fuzzy - Tsallis Entr...

引用

IEEE International Conference on Fuzzy Systems

作者： S. Sarkar S. Das S. Paul S. Polley R. Burman S. S. Chaudhuri ECE Department RCCIIT ECS Unit Indian Statistical Institute ETCE Department Jadavpur University

ISBN: (纸本)9781479900213

关键词： multi-level image segmentation multi-level Thresholding Tsallis Entropy Fuzzy Entropy Differential Evolution MSSIM

来源：评论

学校读者我要写书评

暂无评论

Gaussian kernel probability-driven slime mould algorithm with new movement mechanism for multi-level image segmentation

引用

MEASUREMENT 2022年 192卷 110884-110884页

作者： Ren, Lili Heidari, Ali Asghar Cai, Zhennao Shao, Qike Liang, Guoxi Chen, Hui-Ling Pan, Zhifang Changchun Normal Univ High Performance Comp Ctr Changchun 130032 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China

First, this study develops an enhanced slime mould algorithm (MGSMA). The main idea is to combine the new movement strategy and Gaussian kernel probability strategy to improve the optimization performance of the original slime mould algorithm. These two tactics increase MGSMA's capacity to avoid being stuck in a local optimum and reduce the probability of delaying the convergence process. Second, by integrating non-local mean, 2D Kapur's entropy, and other relevant methodologies, a novel multi-level image segmentation (MLIS) model is developed based on the suggested MGSMA. To showcase MGSMA's performance, specific comparative tests based on IEEE CEC2014 are carried out, clearly showing that MGSMA is a swarm intelligence approach capable of jumping out of the local optimum and the convergence process does not willingly interrupt. To demonstrate that the MGSMA-based MLIS approach can provide high-quality segmentation results, it is compared to eight other comparable methods at both high and low thresholding levels, with some relevant experimental findings to back up its claims. As a consequence, there is no question that MGSMA is a high-performance swarm intelligence optimization approach and that the MGSMA-based MLIS method can provide high-quality segmentation results. The source codes of the SMA algorithm and latest updates are publicly available at https://***/***.

关键词： Global optimization Swarm intelligence Slime mould algorithm multi-level image segmentation 2D Kapur's entropy Benchmark IEEE CEC image

来源：评论

学校读者我要写书评

暂无评论

Hyper-spectral image segmentation using Renyi entropy based multi-level thresholding aided with differential evolution

引用

EXPERT SYSTEMS WITH APPLICATIONS 2016年 50卷 120-129页

作者： Sarkar, Soham Das, Swagatam Chaudhuri, Sheli Sinha RCC Inst Informat Technol Dept Elect & Commun Engn Kolkata 700015 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India

This article presents a novel approach for unsupervised classification of land cover study of hyper-spectral satellite images to improve separation between objects and background by using multi-level thresholding based on the maximum Renyi entropy (MRE). multi-level thresholding, which partitions a gray-level image into several distinct homogeneous regions, is a widely popular tool for segmentation. However, utility of multi-level thresholding is yet to be investigated in challenging applications like hyper-spectral image analysis. Differential Evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques. In addition, the outcomes of the MRE-based thresholding are employed to train a Support Vector Machine (SVM) classifier via the composite kernel approach to improve the classification accuracy. The final outcomes are tested on popular hyper-spectral imagery like ROSIS and AVRIS sensors. The effectiveness of the proposed algorithm is evaluated through qualitative and quantitative comparison with other state-of-the-art global optimizers. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Differential Evolution (DE) Hyper-spectral image multi-level image segmentation Renyi entropy multiple learning kernel Support Vector Machine (SVM)

来源：评论

学校读者我要写书评

暂无评论

A Local Contrast Fusion Based 3D Otsu Algorithm for multilevel image segmentation

引用

IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 200-213页

作者： Ashish Kumar Bhandari Arunangshu Ghosh Immadisetty Vinod Kumar Department of Electronics and Communication Engineering National Institute of Technology Patna

To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been *** all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.

关键词： 1D Otsu 2D Otsu 3D Otsu image fusion local contrast multi-level image segmentation

来源：评论

学校读者我要写书评

暂无评论

Spatial context cross entropy function based multilevel image segmentation using multi-verse optimizer

引用

multiMEDIA TOOLS AND APPLICATIONS 2019年第16期78卷 22613-22641页

作者： Kandhway, Pankaj Bhandari, Ashish Kumar Natl Inst Technol Dept Elect & Commun Engn Patna 800005 Bihar India

In this paper, a context-sensitive energy curve based cross-entropy method for multilevel color image segmentation is proposed. In thresholding approaches, pixels are arranged in various regions based on their intensity level. The main challenge generally faced in multilevel thresholding is the selection of best threshold values for the pixel division. However, the combination of the energy curve and the minimum cross entropy (Energy-MCE) scheme provides appropriate thresholds for a multilevel approach, but the computational cost for selecting optimal thresholds is high. Therefore, the selection of meta-heuristic optimization algorithms reduces this cost and generates optimal thresholds. A multi-verse optimizer (MVO) algorithm based on Energy-MCE thresholding approach is proposed to search the accurate and near-optimal thresholds for segmentation. Tests on natural images showed that the proposed method achieves better performance than the well-known optimization techniques in many challenging cases or images, such as identifying weak objects and revealing fine structures of complex objects while the added computational cost is minimal.

关键词： Minimum cross entropy multi-verse optimizer Energy curve multi-level image segmentation

来源：评论

学校读者我要写书评

暂无评论

A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution

引用

PATTERN RECOGNITION LETTERS 2015年第Mar.1期54卷 27-35页

We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding techniques are widely popular for segmenting grayscale images. Color image segmentation is still a challenging field as it involves 3-D histogram unlike the 1-D histogram of grayscale images. Effectiveness of entropy based multi-level thresholding for color image is yet to be explored and this paper presents a humble contribution in this context. We have used differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The proposed method is evaluated by comparing it with seven other prominent algorithms both qualitatively and quantitatively using a well known benchmark suite - the Barkley segmentation Dataset (BSDS300) with 300 distinct images. Such comparison reflects the efficiency of our algorithm (C) 2014 Published by Elsevier B.V.

关键词： multi-level image segmentation Color image Minimum cross entropy thresholding Differential evolution Berkeley segmentation Dataset and Benchmark

来源：评论

学校读者我要写书评

暂无评论

A multilevel color image thresholding scheme based on minimum cross entropy and alternating direction method of multipliers

引用

OPTIK 2019年 183卷 30-37页

作者： Li, Jiafu Tang, Wenyan Wang, Jun Zhang, Xiaolin Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Heilongjiang Peoples R China Natl Inst Metrol Precis Measurement Lab Beijing 100013 Peoples R China

In this paper, we focus on the problem of fast color image segmentation and propose a two-stage image segmentation method using multi-level thresholding algorithm and statistical region merging (SRM) technique. With the help of the alternating direction method of multipliers (ADMM), the optimal threshold values can be determined by the centroid of sub-modes of the image histogram that follow minimum cross entropy (MEC). In the second stage, a modified SRM is used to remove over-segmented regions of thresholded image to bring the final result. Experimental results depicts that the proposed approach selects threshold values more efficiently as compared to other MCE-based thresholding techniques and produces high quality of the segmented color images than the other methods like mean-shift, normalized cuts (Ncuts) and differential evolution (Q = 7).

关键词： multi-level image segmentation Color image Minimum cross entropy Altemating direction method of multipliers

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：