MANAGING DIGITAL PHOTOGRAPHY ASSETS WITH AI TOOLS

Vikrant Nangare; Chandrashekhar Ramesh Ramtirthkar; Keerthika K; Gajanan P Arsalwad; Priyadarshani Singh; Prabhakar Sharma

doi:10.29121/shodhkosh.v7.i1s.2026.7116

Authors

Dr. Vikrant Nangare Assistant Professor, Bharati Vidyapeeth (Deemed to be university), Institute of Management and Entrepreneurship Development, Pune 411038, India
Chandrashekhar Ramesh Ramtirthkar Associate Professor, Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
Keerthika K Assistant Professor, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600099, India
Dr. Gajanan P Arsalwad Assistant Professor, Department of Computer Engineering, Trinity College of Engineering and Research, Pune, India
Priyadarshani Singh Associate Professor, School of Business Management, Noida International University, Greater Noida 203201, India
Prabhakar Sharma Department of Artificial Intelligence and Machine Learning, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, Chhattisgarh, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7116

Keywords:

Digital Asset Management, Artificial Intelligence, Image Classification, Multimodal Learning, Automated Tagging, Photography Workflows

Abstract [English]

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.

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