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Image Classification Approach Based on Manifold Learning in Web Image Mining

机译:Web图像挖掘中基于流形学习的图像分类方法

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摘要

Automatic image classification is a challenging research topic in Web image mining. In this paper, we formulate image classification problem as the calculation of the distance measure between training manifold and test manifold. We propose an improved nonlinear dimensionality reduction algorithm based on neighborhood optimization, not only to decrease feature dimensionality but also to transform the problem from high-dimensional data space into low-dimensional feature space. Considering that the images in most real-world applications have large diversities within category and among categories, we propose a new scheme to construct a set of training manifolds each representing one semantic category and partition each nonlinear manifold into several linear sub-manifolds via region growing. Moreover, to further reduce computational complexity, each sub-manifold is depicted by aggregation center. Experimental results on two Web image sets demonstrate the feasibility and effectiveness of the proposed approach.
机译:自动图像分类是Web图像挖掘中一个具有挑战性的研究主题。在本文中,我们将图像分类问题公式化为训练流形和测试流形之间的距离度量的计算。我们提出了一种基于邻域优化的改进的非线性降维算法,不仅可以降低特征维数,而且可以将问题从高维数据空间转化为低维特征空间。考虑到大多数现实应用中的图像在类别之间和类别之间具有较大的差异,我们提出了一种新方案,以构造一组训练流形,每个训练流形表示一个语义类别,并通过区域增长将每个非线性流形划分为几个线性子流形。此外,为了进一步降低计算复杂度,每个子流形都由聚合中心表示。在两个Web图像集上的实验结果证明了该方法的可行性和有效性。

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