摘要
视觉词典法是当前广泛使用的一种图像表示方法,针对传统视觉词典法存在的表示误差大、空间信息丢失以及判别性弱等问题,提出一种基于Fisher判别稀疏编码的图像场景分类算法.首先利用近邻视觉词汇重构局部特征点,构建局部特征点的非负稀疏局部线性编码,从而有效地利用图像的空间信息;然后在非负稀疏局部线性编码的基础上引入Fisher判别约束准则,构建基于Fisher判别约束的非负稀疏局部线性编码模型,以获得图像的判别稀疏向量表示,增强图像稀疏表示的判别性;最后结合支持向量机(SVM)分类器实现场景分类.实验结果表明,该算法提高了图像稀疏表示的特征分类能力以及分类性能,更有利于场景分类任务.
Bag of visual word （BoVW） is widely utilized as an image representation model. However, con-ventional BoVW construction methods usually cause large representation errors, lack of spatial information and weak discrimination. In order to overcome these drawbacks, this paper proposes an image scene classi-fication algorithm based on fisher discriminative analysis and sparse coding. Firstly, the non-negative sparse locally linear coding is constructed to encode the local features with their neighbor visual vocabularies, thus to make full use of images’ spatial information. Secondly, fisher discriminative analysis is added to construct a non-negative sparse locally linear coding model with fisher discriminative criterion constraint, thus to ob-tain the discriminative sparse representation of images. The novel model can promote the spatial separability of sparse coefficients and enforce the classification capability of images’ sparse representation. Finally, support vector machine （SVM） classifier is combined to perform scene classification. Experimental results show that our algorithm efficiently utilizes spatial information of images and incline to seek images’ dis-crimination representations, thus improves the classification performance and is more suitable for image classification.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2015年第5期808-814,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61309016)
关键词
场景分类
图像表示
非负稀疏局部线性编码
Fisher判别约束准则
scene classification
image representation
non-negative sparse locally linear coding
fisher dis-criminative criterion constraint
