An Improvable Structure for Similarity Searching in Metric Spaces: Application on Image Databases

In last decades, the similarity search is very required in various fields such as pattern recognition, security, and multimedia databases. Although the metric approach usefulness for speeding similarity search in complex databases, the searching cost optimization still an open problem. In this paper we propose an improvable pivot-based method which can improve its research efficiency based on the past users' queries. Because images are the most data type which are concerned by the similarity search, the proposed method is tested on a real images database. The experiments show that the proposed method can significantly improve its searching efficiency relying on queries resolution.