Local complex features learned by randomized neural networks for texture analysis

IF 3.7 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Analysis and Applications Pub Date : 2024-02-28 DOI:10.1007/s10044-024-01230-x

引用次数: 0

Abstract

Texture is a visual attribute largely used in many problems of image analysis. Many methods that use learning techniques have been proposed for texture discrimination, achieving improved performance over previous handcrafted methods. In this paper, we present a new approach that combines a learning technique and the complex network (CN) theory for texture analysis. This method takes advantage of the representation capacity of CN to model a texture image as a directed network and then uses the topological information of vertices to train a randomized neural network. This neural network has a single hidden layer and uses a fast learning algorithm to learn local CN patterns for texture characterization. Thus, we use the weights of the trained neural network to compose a feature vector. These feature vectors are evaluated in a classification experiment in four widely used image databases. Experimental results show a high classification performance of the proposed method compared to other methods, indicating that our approach can be used in many image analysis problems.

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用随机神经网络学习局部复杂特征进行纹理分析

摘要纹理是一种视觉属性，在许多图像分析问题中得到广泛应用。许多利用学习技术进行纹理判别的方法已被提出，与以前的手工方法相比，这些方法的性能有所提高。在本文中，我们提出了一种结合学习技术和复杂网络（CN）理论进行纹理分析的新方法。该方法利用复杂网络的表示能力，将纹理图像建模为有向网络，然后利用顶点的拓扑信息训练随机神经网络。该神经网络只有一个隐藏层，使用快速学习算法来学习局部 CN 模式，从而进行纹理表征。因此，我们使用训练好的神经网络的权重来组成特征向量。我们在四个广泛使用的图像数据库中对这些特征向量进行了分类评估。实验结果表明，与其他方法相比，所提出的方法具有很高的分类性能，这表明我们的方法可用于许多图像分析问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Pattern Analysis and Applications 工程技术-计算机：人工智能

CiteScore

7.40

自引率

2.60%

发文量

审稿时长

13.5 months

期刊介绍： The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.