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.

Feature representation and compression for contentbased retrieval In semantic contentbased imagevideo browsing and navigation systems, efficient mechanisms to represent and manage a large collection of digital imagesvideos are needed. Traditional keywordbased indexing describes the content of multimedia data through annotations such as text or keywords extracted manually by the user from a controlled vocabulary. This textual indexing technique lacks the flexibility of satisfying various kinds of queries requested by database users and also requires huge amount of work for updating the information. Current contentbased retrieval systems often extract a set of features such as color, texture, shape motion, speed, and position from the raw multimedia data automatically and store them as content descriptors. This contentbased metadata differs from textbased metadata in that it supports wider varieties of queries and can be extracted automatically, thus providing a promising approach for efficient database access and management. When the raw data volume grows very large, explicitly extracting the contentinformation and storing it as metadata along with the images will improve querying performance since metadata requires much less storage than the raw image data and thus will be easier to manipulate. In this paper we maintain that storing metadata together with images will enable effective information management and efficient remote query. We also show, using a texture classification example, that this side information can be compressed while guaranteeing that the desired query accuracy is satisfied. We argue that the compact representation of the image contents not only reduces significantly the storage and transmission rate requirement, but also facilitates certain types of queries. Algorithms are developed for optimized compression of this texture feature metadata given that the goal is to maximize the classification performance for a given rate budget.