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Rough-Set and Artificial Neural Networks Based Image Classification

机译:基于粗糙集和人工神经网络的图像分类

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Spatial image classification meant to the mechanism of extracting meaningful knowledge information classes from spatial images dataset. Many traditional pixel based image classification techniques such as Support Vector Machines (SVM), ANN, Fuzzy methods, Decision Trees (DT) etc. exist. The performance and accuracy of these image classification methods depends upon the network structure and number of inputs. Here, in this paper, we have proposed an step-wise mechanism to significantly improve the classification performance of neural network, that uses rough sets approach for purpose of features/attributes selection of image pixels. The complexity analysis of the proposed algorithm and the comparison of mechanism, presented here, with existing classification techniques based on features over the interest area is carried out.
机译:空间图像分类意味着从空间图像数据集中提取有意义的知识信息类的机制。存在许多传统的基于像素的图像分类技术,例如支持向量机(SVM),ANN,模糊方法,决定树(DT)等。这些图像分类方法的性能和准确性取决于网络结构和输入数。在这里,在本文中,我们提出了一种逐步改善神经网络的分类性能的逐步机制,其使用粗糙集方法来用于图像像素的特征/属性选择。提出了基于兴趣区上的特征的所提出的算法的复杂性分析及其机制的比较。

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