A KFCM and SIFT Based Matching Approach to Similarity Retrieval of Images
Recently, keypoint descriptors such as Scale Invariant Feature Transform (SIFT) have been proved promising in similarity retrieval of images, which adopts matching score as similarity. However, the matching score is easy to be decreased once there are little variances between image details, and hence lead to low retrieval performance. In this paper, we propose a novel retrieval approach that improves the matching score with reduced time of matching by Kernel-based Fuzzy C-Means clustering (KFCM), which proves to be a better trade-off between matching and retrieval precision. Experiments conducted on three representative image databases show that our retrieval approach is surprisingly effective, outperforming the SIFT based method, not only in object-based image retrieval but also for searching scenes with similar semantic.
Pengyi Hao Youdong Ding Yuchun Fang Ranran Zhang Shuhan Wei
School of Computer Engineering and Science, ShangHai University, ShangHai, 200072, China
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
372-377
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）