MANAGING DIGITAL PHOTOGRAPHY ASSETS WITH AI TOOLS - Nangare, Vikrant; Ramtirthkar, Chandrashekhar Ramesh; Keerthika K; Arsalwad, Gajanan P; Singh, Priyadarshani; Sharma, Prabhakar (ShodhKosh: Journal of Visual and Performing Arts, 2026)
The management of high-resolution photographs collections in large quantities has been complicated by the fact that the volumes of images, their heterogeneous file formats and full but usually sporadic metadata has grown exponentially. Manual and rule-based Digital Asset Management (DAM) systems are not conducive to the efficient organization, retrieval and re-use of photographic assets, especially by the professional photographer and creative industries that have a restrictive production schedule. The paper suggests an AI-powered model of digital photography management based on introducing the latest developments in computer vision, multimodal learning, and natural language processing. It uses the models of deep learning based on image classification, such as convolutional neural networks and vision transformer, to process visual information automatically and retrieve high-level semantic information. Such characteristics are incorporated in shared representation spaces to facilitate semantic clustering, similarity search and context based organization. Simultaneously, natural language processing methods are used to create descriptive labels and tags by combining visual intelligence with additional hidden data like EXIF information, capture settings and geospatial position. The offered system also includes image quality scoring and aesthetic evaluation modules to help photographers to curate, rank, and choose images according to technical and artistic requirements.