基于自适应支持向量机和随机决策树的图像检索

Q3 Medicine
Xin-xin Xie, Wenzhun Huang, H. Wang, Zhe Liu
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引用次数: 6

摘要

本文对基于支持向量机和决策树的图像检索算法进行了研究。图像数据库检索系统是图像数据库的核心部分,该系统采用一定的图像算法对数据库中的图像数据进行变换、操作和组织,并与完整的图像数据库检索算法相连接,实现图像检索功能,从而获得检索结果,满足用户的需求,满足用户的需求。具有形状、纹理、颜色等特征的数据,这就决定了图像数据库具有不同于传统数据库检索的方式。为了提高图像数据库检索的效率,必须精心设计图像数据库检索系统的结构,采用高效快速的图像检索方法。我们的研究为相关问题的研究提供了新的视角,从而获得了可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Retrieval with Adaptive SVM and Random Decision Tree
In this paper, we conduct research on the image retrieval algorithm based on the support vector machine and the decision tree. Image database retrieval system is the core part of the image database, the system uses a certain algorithm of image to transform the image data in the database, operation and organization, and connecting with the complete image database retrieval algorithm of the image retrieval function, in order to obtain the retrieval results, to meet the needs of users to meet the needs of its users. Have the feature such as shape, texture, color data, which determines the image database has a different way of conventional database retrieval. In order to improve the efficiency of the image database retrieval, must be carefully designed the structure of image database retrieval system, adopt efficient image retrieval method quickly. Our research proposes the novel perspectives of the related issues that obtain the feasible and effective.
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来源期刊
Koomesh
Koomesh Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
0
审稿时长
24 weeks
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