摘要
已有的针对上下文信息的大多数工作均侧重于视觉词之间的上下文信息建模,没有考虑到局部特征之间的上下文信息建模问题,且图像在拍照时往往受到姿势、尺度变化,光照以及相机参数的影响,导致分类精度不高.文中综合考虑局部特征之间的上下文信息,提出一种基于有判别力仿射局部特征上下文的图像分类方法.对于一幅图像上的某一位置,采用该区域的局部特征,及其周边一定距离、角度内的局部特征来进行描述(局部特征上下文);然后对这些局部特征上下文进行仿射变换,并通过最小化编码损失的策略来进行有判别力的仿射局部特征上下文的选择,得到更有判别力的特征.最后通过实验结果验证了该方法的有效性.
Most of context based methods focus on using context information at the visual word level without considering the relationship between local features. Besides, images are often captured with various poses, scale changes, illumination variation and camera parameters. This hinders the improvement of image classification performance. By combining contextual information of local features, this paper proposes a novel discriminative affine local feature context method for efficient image classification. We use the local feature at the position as well as other local features based on their distances and angels to this position. Affine transformations are done to the local feature context in order to get more robust and effective features. The discriminative affine-transformed local feature context is then chosen by minimizing the reconstruction error. Classification experiments demonstrate the effectiveness of the proposed method.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2014年第5期762-766,共5页
Journal of Computer-Aided Design & Computer Graphics
基金
国家"九七三"重点基础研究发展计划项目(2012CB316400)
国家自然科学基金(61025011
61303154
61332016
61202325
61202322)
模式识别国家重点实验室开放课题(201204268)
中国科学院大学校长基金
关键词
局部特征上下文
仿射不变性
稀疏编码
图像分类
local feature context~ affine invariant~ sparse coding
image classification
