Defining Similarity Spaces for Large-Scale Image Retrieval Through Scientific Workflows

Content-Based Image Retrieval (CBIR) employs visual features from images for searching and retrieving of data. Systems based on this concept depend on a similarity space instance definition, but achieving an ideal instance is a very complex process and is dependent on domain knowledge. At the same time, domain experts are often unable to interact fully with systems because of technicalities. In this paper, we propose an architecture, based on scientific workflows, which allows users with no prior programming experience to build processes on images, creating Similarity Spaces and evaluating them when running similarity queries. Through this architecture, they can use domain expertise to improve image retrieval in a coordinated, auditable and reproducible manner, while being able to process very large image collections. We describe a prototype system and carry out experiments evaluating its performance in various scenarios. The current implementation supports both similarity space definition and querying workflows, achieving suitable speedups with the increase in the number of machines.