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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A Novel Local Human Visual Perceptual Texture Description with Key Feature Selection for Texture Classification
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A Novel Local Human Visual Perceptual Texture Description with Key Feature Selection for Texture Classification

机译：一种新的本地人类视觉感知纹理描述，具有纹理分类的关键特征选择

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This paper proposes a novel local texture description method which defines six human visual perceptual characteristics and selects the minimal subset of relevant as well as nonredundant features based on principal component analysis (PCA). We assign six texture characteristics, which were originally defined by Tamura et al., with novel definition and local metrics so that these measurements reflect the human perception of each characteristic more precisely. Then, we propose a PCA-based feature selection method exploiting the structure of the principal components of the feature set to find a subset of the original feature vector, where the features reflect the most representative characteristics for the textures in the given image dataset. Experiments on different publicly available large datasets demonstrate that the proposed method provides superior performance of classification over most of the state-of-the-art feature description methods with respect to accuracy and efficiency.
机译：本文提出了一种新颖的局部纹理描述方法，其定义了六种人类视觉感知特征，并根据主成分分析（PCA）选择相关的最小和非冗余特征子集。我们分配六种纹理特征，最初由Tamura等人定义。，具有新的定义和局部指标，使得这些测量更精确地反映了每个特征的人类感知。然后，我们提出了一种基于PCA的特征选择方法，该方法利用特征集的主组件的结构来查找原始特征向量的子集，其中特征反映给定图像数据集中的纹理的最代表性特征。在不同公开的大型数据集上的实验表明，该方法在大多数最先进的特征描述方法方面提供了优异的分类性能，以方面的准确性和效率。

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