摘要
针对复杂环境中的行人检测问题,提出了一种有效的基于分层稀疏编码的图像表示方法。首先通过两层稀疏编码模型结合基于K-SVD的深度学习算法来获得图像的稀疏表示,对图像块及同一区域的高阶依赖关系进行了建模,形成一个有效的无监督特征学习方法;然后将得到的稀疏表示与SIFT描述符的稀疏表示进行特征融合,得到了更加全面、更加可判别的图像表示;最后结合SVM分类器应用于行人分类任务。实验结果表明,该行人分类方法对比同类方法在性能上有明显改善。
Aiming at pedestrian detection problem in complex environments, we propose an effective image representation method based on hierarchical sparse coding. First, we obtain the sparse representation by a two-layer sparse coding model combined with a K-SVD based deep learning algorithm. We then model image blocks and higher-order dependencies of the same region, forming an effective unsupervised feature learning method. After that, we fuse the sparse representation with the sparse representation of the SIFT descriptor, obtaining a more comprehensive and more discriminant image representation. Finally, together with the SVM classifier, it is applied to pedestrian classification tasks. Experimental results show that the pedestrian classification method has very competitive performance in comparison with other similar methods.
出处
《计算机工程与科学》
CSCD
北大核心
2016年第10期2115-2120,共6页
Computer Engineering & Science
基金
国家自然科学基金(61471154)
教育部留学回国人员科研启动基金
