Applying I-FGM to image retrieval and an I-FGM system performance analyses

Intelligent Foraging, Gathering and Matching (I-FGM) combines a unique multi-agent architecture with a novel partial processing paradigm to provide a solution for real-time information retrieval in large and dynamic databases. I-FGM provides a unified framework for combining the results from various heterogeneous databases and seeks to provide easily verifiable performance guarantees. In our previous work, I-FGM had been implemented and validated with experiments on dynamic text data. However, the heterogeneity of search spaces requires our system having the ability to effectively handle various types of data. Besides texts, images are the most significant and fundamental data for information retrieval. In this paper, we extend the I-FGM system to incorporate images in its search spaces using a region-based Wavelet Image Retrieval algorithm called WALRUS. Similar to what we did for text retrieval, we modified the WALRUS algorithm to partially and incrementally extract the regions from an image and measure the similarity value of this image. Based on the obtained partial results, we refine our computational resources by updating the priority values of image documents. Experiments have been conducted on I-FGM system with image retrieval. The results show that I-FGM outperforms its control systems. Also, in this paper we present theoretical analysis of the systems with a focus on performance. Based on probability theory, we provide models and predictions of the average performance of the I-FGM system and its two control systems, as well as the systems without partial processing.