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    梁天一, 宋国新, 虞慧群. 基于稀疏编码的图像语义分类器模型[J]. 华东理工大学学报(自然科学版), 2007, (6): 827-830892.
    引用本文: 梁天一, 宋国新, 虞慧群. 基于稀疏编码的图像语义分类器模型[J]. 华东理工大学学报(自然科学版), 2007, (6): 827-830892.
    LIANG Tian-yi, SONG Guo-xin, YU Hui-qun. Sparse Coding-Based Image Semantic Classifier Model[J]. Journal of East China University of Science and Technology, 2007, (6): 827-830892.
    Citation: LIANG Tian-yi, SONG Guo-xin, YU Hui-qun. Sparse Coding-Based Image Semantic Classifier Model[J]. Journal of East China University of Science and Technology, 2007, (6): 827-830892.

    基于稀疏编码的图像语义分类器模型

    Sparse Coding-Based Image Semantic Classifier Model

    • 摘要: 为了解决图像检索以及遥感图像识别等图像处理研究中本质的问题——如何对高层抽象图像语义进行有效的分类,本文采用生物视觉认知机理,结合生物特征信息,用最小生成树的方法构造图像信息语义树,提出了一个基于稀疏编码的图像语义分类器(SCISC)的模型。实验结果表明:该模型在图像分类中有较高正确率。

       

      Abstract: In order to solve the problem in the image process researches,it is very important to find an efficient,biologic recognition related,and high-level semantic image classification method.By adopting the theories of biologic visual recognition and knowledge engineering combined with biologic characteristic(information,) and using minimum spanning tree as an image information construction tree,this paper pre-(sents) a sparse coding-based image semantic classifier (SCISC) model.The experimental result shows that this model has high accuracy in image classification.

       

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