Glaucoma patients experience difficulty with daily activities, including reading, mobility, and driving. Visual search is instrumental to rapidly localizing target objects among competing objects within our environment. While numerous studies have evaluated factors that influence visual search performance, limited evidence is available for how these factors affect visual search in glaucoma patients. Our core hypothesis is that environmental scene context and image characteristics fundamentally influence visual search performance in glaucoma. We propose a conceptual model for evaluating visual search performance in glaucoma patients using deep learning and image processing techniques. We utilize the Berlin Object in Scene Database (BOiS) to test the model. This database contains 130 high-resolution naturalistic images intended to evaluate visual search performance in a real-world setting. Our results describe the scene context and image characteristics of each scene, which will help to evaluate and understand the perception of glaucoma patients during visual search in future research.