CAJ | 학술논문

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.
시각사전법시당전엄범사용적일충도상표시방법, 침대전통시각사전법존재적표시오차대、공간신식주실이급판별성약등문제, 제출일충기우 Fisher 판별희소편마적도상장경분류산법. 수선이용근린시각사회중구국부특정점, 구건국부특정점적비부희소국부선성편마, 종이유효지이용도상적공간신식; 연후재비부희소국부선성편마적기출상인입Fisher판별약속준칙, 구건기우Fisher판별약속적비부희소국부선성편마모형, 이획득도상적판별희소향량표시, 증강도상희소표시적판별성; 최후결합지지향량궤(SVM)분류기실현장경분류. 실험결과표명, 해산법제고료도상희소표시적특정분류능력이급분류성능, 경유리우장경분류임무.
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.