Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

Optimized image feature selection using pairwise classifiers

In this paper, we introduce an optimized method to improve the accuracy of content based image retrieval systems (CBIR). CBIR systems classify the images according to low and higher features.In our research, we improve both feature selection and classifier partition of a CBIR system. Results show great performance of our proposed algorithm

A Reference Architecture for an Enterprise Knowledge Infrastructure

Part 6: Enterprise Systems IntegrationInternational audienceWith the emergence of social media, data available for market analytics has grown significantly, especially in the context of product lifecycle value analysis. Existing architecture frameworks do not support knowledge management, required to process massive amount of market data, or ’big data’. In order to perform product lifecycle value analysis, product managers need to access, in a seamless manner, data from several domains from systems within the organization and from external sources such as social media, government and industry sites, to name a few. The structured and integrated data must then be transformed into information, or contextualized data, and ultimately into actionable information or knowledge. To achieve this objective, this paper proposes an innovative approach, the Reference Architecture of an Enterprise Knowledge Infrastructure (RA-EKI) that provides a holistic approach to manage the complete knowledge lifecycle