BIRDS-I: A Benchmark for Image Retrieval Using Distributed Systems over the Internet

Title: BIRDS-I: A Benchmark for Image Retrieval Using Distributed Systems over the Internet

Abstract: BIRDS-I is a benchmark designed to measure the performance of content-based image retrieval (CBIR) systems in a distributed network environment. It is particularly suited for web-based CBIR applications, which rely on heterogeneous image sets. The benchmark uses a client-server model and requires minimal human intervention, making it ideal for large-scale, personalized wireless-internet systems. BIRDS-I introduces a tightly optimized image-ranking window, which is crucial for the future benchmarking of large-scale CBIR systems.

Research Question: How can we measure the performance of content-based image retrieval systems in a distributed network environment, particularly for web-based applications?

Methodology: BIRDS-I uses a client-server model, where the client sends a query to the server, which then retrieves and ranks the images based on the query. The benchmark uses a controlled human intervention for image compilation but none for ground truth generation. It introduces a tightly optimized image-ranking window, which is essential for large-scale, personalized wireless-internet CBIR systems.

Results: BIRDS-I has been successfully implemented and tested. It has shown promising results in measuring the performance of CBIR systems in a distributed network environment. The benchmark has been designed with scalability in mind, allowing for the storage of millions of images.

Implications: BIRDS-I provides a standardized method for comparing the performance of CBIR systems, particularly for web-based applications. It encourages the development of more efficient and accurate CBIR systems, which can lead to improved user experience in image search and retrieval. Additionally, it sets a precedent for other distributed system benchmarks, demonstrating the importance of networked system benchmarks in real-world environments.

Link to Article: https://arxiv.org/abs/0012021v1 Authors: arXiv ID: 0012021v1