🤖 AI Summary
This study addresses the prevalent issue in computer vision datasets wherein image provenance—such as acquisition parameters and preprocessing steps—is often missing or stored separately, leading to compromised traceability, regulatory compliance, and data reusability. To overcome this limitation, the work proposes a novel approach that leverages JSON-LD (JavaScript Object Notation for Linked Data) to define a structured provenance schema and embeds it directly within image files. This integration ensures an inseparable binding between metadata and the image payload, preserving provenance integrity and persistence across workflows. The proposed method maintains compatibility with existing standards while significantly enhancing data maintainability, system interoperability, and the trustworthiness of downstream models trained on such annotated datasets.
📝 Abstract
With the ubiquity of computer vision in industry, the importance of image provenance is becoming more apparent. Provenance provides information about the origin and derivation of some resource, e.g., an image dataset, enabling users to trace data changes to better understand the expected behaviors of downstream models trained on such data. Provenance may also help with data maintenance by ensuring compliance, supporting audits and improving reusability. Typically, if provided, provenance is stored separately, e.g., within a text file, leading to a loss of descriptive information for key details like image capture settings, data preprocessing steps, and model architecture or iteration. Images often lack the information detailing the parameters of their creation or compilation. This paper proposes a novel schema designed to structure image provenance in a manageable and coherent format. The approach utilizes JavaScript Object Notation for Linked Data (JSON-LD), embedding this provenance directly within the image file. This offers two significant benefits: (1) it aligns image descriptions with a robust schema inspired by and linked to established standards, and (2) it ensures that provenance remains intrinsically tied to images, preventing loss of information and enhancing system qualities, e.g., maintainability and adaptability. This approach emphasizes maintaining the direct connection between vision resources and their provenance.