摘要
为了有效地组织、管理和浏览大规模的图像资源,提出了一种利用局部特征进行图像分类的方法。通过深入分析和比较常见的局部特征,选用合适的局部特征构建视觉单词库。这些视觉单词具有很好的平移、旋转、尺度不变性,并对噪声有一定的抵抗能力。借鉴文本分类领域的向量空间模型进行图像的表示,并设计出了相应的分类算法。标准图像库上的实验结果表明,该方法在图像分类中有效,有较高的实用价值。
In order to organize, manage and browse large-scale image databases effectively, an image classification algorithm based on local features is proposed. After analyzing of several fashionable local features at present, we choose the suitable features to construct the visual vocabulary. These visual words are invariant to image scale and rotation, and are shown robust to addition of noise and changes in 3D viewpoint. We also describe two approaches to represent objects using these visual words. As baselines for comparison, some additional classification systems also have been implemented. The performance analysis on the obtained experimental results demonstrates that the proposed methods are effective and highly valuable in practice.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2017年第1期69-74,共6页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(61402023)
北京市教委科研计划(SQKM201610011010)
北京市自然科学基金(4162019)
北京市科技计划(Z161100001616004)
关键词
凝聚聚类
分类器
图像分类
局部特征
视觉单词
agglomerative clustering
classifier
image classification
local features
visual word
