Combining MSCR Detector and PCA-SIFT Descriptor for Scene Recognition
This paper introduces a novel scene recognition algorithm to perform reliable scene recognition. Firstly, to construct the maximally stable regions, we exploit the maximally stable color regions (MSCR) detector for improving the identification of stable regions. Secondly, each detected region is processed properly by using the method of mathematics morphology. Finally, the descriptor is computed by using the Principal Components Analysis (PCA) based scale invariant feature transform (SIFT) descriptors, with the detected MSCR regions as input Our experiments demonstrate that this algorithm wins high recognition accuracy, is more robust to image deformations and is both significantly more accurate and much faster than the standard SIFT descriptor based algorithm. Also we compare our algorithm to the global appearance based method, and show through experiments in both indoor and outdoor environments that our approach performs better.
scene recognition feature extraction MSCR PCA- SIFT invariant feature
SHI Dong-cheng YAN Guo-qing
School of Computer Science and Engineering Changchun University of Technology Changchun 130012, P.R.China
国际会议
The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)
沈阳
英文
136-141
2010-03-27（万方平台首次上网日期，不代表论文的发表时间）