自组织映射方法和特征选择在图像数据库分类系统中的应用
The Use of Self Organizing Map Method and Feature Selection in Image Database Classification System
摘要：
本文提出了一种利用自组织映射（SOM）人工神经网络方法对图像进行分类的方法。对多幅250色待分类的图像做了一定的处理，如RGB到灰度的颜色转换、颜色直方图、特征向量的选择，然后通过SOM分类器进行特征向量的选择，本文将采用两种方法，即通过PCA（主成分分析）和LSA潜在语义分析（Latent Semantic Analysis）中，这些方法中的每一种都将从256个初始特征向量中取出50、100和150的特征向量进入颜色直方图的过程。然后，选择将被处理到SOM网络中，以使用0.5的学习率和计算的精度将其分类为五类。部分分类测试结果表明，当使用PCA和选择100个特征向量时获得的准确率最高，即等于88%，相比之下，当使用LSA选择时仅为74%。因此可以得出结论，该方法适合PCA特征选择方法与SOM结合应用，并且准确率优于LSA特征选择方法。关键词：颜色直方图，特征选择，LSA，PCA，SOM。
英文摘要：
This paper presents a technique in classifying the images into a number of classes or clusters desired by means of Self Organizing Map (SOM) Artificial Neural Network method. A number of 250 color images to be classified as previously done some processing, such as RGB to grayscale color conversion, color histogram, feature vector selection, and then classifying by the SOM Feature vector selection in this paper will use two methods, namely by PCA (Principal Component Analysis) and LSA (Latent Semantic Analysis) in which each of these methods would have taken the characteristic vector of 50, 100, and 150 from 256 initial feature vector into the process of color histogram. Then the selection will be processed into the SOM network to be classified into five classes using a learning rate of 0.5 and calculated accuracy. Classification of some of the test results showed that the highest percentage of accuracy obtained when using PCA and the selection of 100 feature vector that is equal to 88%, compared to when using LSA selection that only 74%. Thus it can be concluded that the method fits the PCA feature selection methods are applied in conjunction with SOM and has an accuracy rate better than the LSA feature selection methods. Keywords: Color Histogram, Feature Selection, LSA, PCA, SOM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
