FKPCA-SIFT算法在图像匹配中的应用  

Application of FKPCA-SIFT Algorithm in Image Matching

作　　者：张宁丽[1] 马燕[1] 李顺宝 徐衍鲁 程玮[2] 

机构地区：[1]上海师范大学信息与机电工程学院,上海200234 [2]上海师范大学数理学院,上海200234

出　　处：《电视技术》2015年第7期21-24,42,共5页Video Engineering

基　　金：国家自然科学基金项目(61373004)

摘　　要：SIFT是目前广泛应用于目标识别和图像匹配领域的算法,但其在使用过程中存在描述子维数过大、耗时时间长的缺点。针对这个问题,常用的解决办法是利用PCA算法对描述子进行降维,由于PCA是一种线性降维算法,因此它的使用具有局限性。对此,利用模糊K均值算法对其进行改进(称为FKPCA),并用改进的RANSAC算法消除误匹配点。实验结果表明,PCA-SIFT算法和FKPCA-SIFT都很好地保持了SIFT算法原有的优点,具有很高的匹配正确率。但相对于PCASIFT算法,FKPCA-SIFT不仅适用于线性降维也适用于非线性降维,具有更好的匹配精度,拓展了PCA-SIFT算法的适用范围。SIFT algorithm is widely used in the field of object recognition and image matching. But it also has disadvantages that sub-dimension is too large and time-consuming. For this problem, the common solution is using PCA algorithm to reduee dimension of the descriptors. But PCA is a linear dimensionality reduction algorithm, so its use is limited. For this situation, using the fuzzy K-means algorithm to improve it （called FKPCA） and eliminate false matching points with an improved RANSAC algorithm. The experimental results are shown that both of the PCA-SIFT and FKPCA-SIVT algorithm can perfectly keep the original advantages of SIIVF that have a high matching aeeuraey. But compared with the PCA-SIFT algorithm, FKPCA-SIFT ean not only be used to the linear dimension reduction, it can also be applied to non-linear situation. The FKPCA-SIFT has better matehing aeeuracy and ex- pands the application seope of PCA-SIFT.

关 键 词：SIFT PCA-SIFT FKPCA-SIFT RANSAC 线性降维 非线性降维 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]