摘要翻译：
非监督分类在多光谱图像分析中具有非常重要的作用，它能够帮助提取图像的先验知识。像k-means和模糊C-means这样的算法长期以来一直被用于这项任务。计算智能已经被证明是一个重要的领域，它可以帮助建立根据类别分组质量和矢量量化质量评价优化的分类器。一些研究表明，哲学，特别是辩证方法，对新的计算方法的构建起到了重要的启发作用。本文对四种基于辩证法的方法进行了评价：客观的辩证分类器和辩证优化法，用于构建具有最优质量指标的K-均值模型；每一个都有两个版本：一个标准版本和另一个应用最大熵原理得到的版本。将这些方法与k-means、fuzzy c-means和Kohonen的自组织映射进行了比较。结果表明，基于辩证法的方法对噪声具有较强的鲁棒性，量化可以达到与Kohonen映射一样好的效果，被认为是一种最优量化器。
---
英文标题：
《Avalia\c{c}\~ao do m\'etodo dial\'etico na quantiza\c{c}\~ao de imagens
  multiespectrais》
---
作者：
Wellington Pinheiro dos Santos, Francisco Marcos de Assis
---
最新提交年份：
2017
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--
一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--

---
英文摘要：
  The unsupervised classification has a very important role in the analysis of multispectral images, given its ability to assist the extraction of a priori knowledge of images. Algorithms like k-means and fuzzy c-means has long been used in this task. Computational Intelligence has proven to be an important field to assist in building classifiers optimized according to the quality of the grouping of classes and the evaluation of the quality of vector quantization. Several studies have shown that Philosophy, especially the Dialectical Method, has served as an important inspiration for the construction of new computational methods. This paper presents an evaluation of four methods based on the Dialectics: the Objective Dialectical Classifier and the Dialectical Optimization Method adapted to build a version of k-means with optimal quality indices; each of them is presented in two versions: a canonical version and another version obtained by applying the Principle of Maximum Entropy. These methods were compared to k-means, fuzzy c-means and Kohonen's self-organizing maps. The results showed that the methods based on Dialectics are robust to noise, and quantization can achieve results as good as those obtained with the Kohonen map, considered an optimal quantizer.
---
PDF链接：
https://arxiv.org/pdf/1712.01696