Reproducibility in computer vision: Towards open publication of image analysis experiments as semantic workflows

Reproducibility of research is an area of growing concern in computer vision. Scientific workflows provide a structured methodology for standardized replication and testing of state-of-the-art models, open publication of datasets and software together, and ease of analysis by re-using pre-existing components. In this paper, we present initial work in developing a framework that will allow reuse and extension of many computer vision methods, as well as allowing easy reproducibility of analytical results, by publishing dadasets and workflows packaged together as linked data. Our approach uses the WINGS semantic workflow system which validates semantic constraints of the computer vision algorithms, making it easy for non-experts to correctly apply state-of-the-art image processing methods to their data. We show the ease of use of semantic workflows for reproducibility in computer vision by both utilizing pre-developed workflow fragments and developing novel computer vision workflow fragments for a video activity recognition task, analysis of multimedia web content, and the analysis of artistic style in paintings using convolutional neural networks.