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.

中文翻译：

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通过随机神经网络学习的局部复杂特征进行纹理分析

摘要

纹理是一种视觉属性，广泛应用于图像分析的许多问题中。已经提出了许多使用学习技术的方法来进行纹理区分，与以前的手工方法相比，性能得到了提高。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CN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来训练随机神经网络。该神经网络具有单个隐藏层，并使用快速学习算法来学习局部 CN 模式以进行纹理表征。因此，我们使用训练后的神经网络的权重来组成特征向量。这些特征向量在四个广泛使用的图像数据库的分类实验中进行评估。实验结果表明，与其他方法相比，该方法具有较高的分类性能，表明我们的方法可以用于许多图像分析问题。