Performance Comparison Between SURF and SIFT for ContentBased Image Retrieval SpeededUp Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) have been two wellknown methods used in extracting features. This paper presents and analyzes performance comparison between the SURF approach and the SIFT technique for contentbased image retrieval (CBIR) application. In particular, we are interested in comparing the accuracy and the response time between these two methods. For the testing purposes, we make use sample images obtained for the Pennsylvania State College of Information Science and Technology database. As it turns out, in this paper, we will demonstrate that in terms of accuracy and speed, SURF shows superior performance compared to SIFT.

Multilayer Architecture for ContentBased Image Retrieval Systems In this paper, we present a novel architecture for contentbased image retrieval systems. Effective storing, browsing and searching collections of images is one of the most critical challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as database and serviceoriented frameworks. With the vast growth of the Internet multimedia content, web services for CBIR systems are highly desirable. The proposed solution is based on a multilayer architecture, which allows replacing any component without recompilation of other components. The approach is elastic and highly scalable. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and kmeans clustering as the image indexer. We also adopted an external solution, i.e. the CEDD descriptor into our system. The presented architecture is intended for contentbased retrieval experiments as well as for realworld CBIR desktop and web applications.