Artificial Intelligence-Driven Classification of Visual Content

Abstract - In fact, image classification is one such important area of machine learning and finds applications in digital curation, automated moderation, and compiling an artistic gallery. This paper is a survey on machine learning methods for differentiating artistic images from non-artistic ones. It adds to suffer from a problem of subjectivity, having various distinct artistic styles, and can have biases in the data set. It combines traditional extraction of features into deep learning models and reaches improved classification accuracy with robust performance for applications in art galleries, media platforms, and automated filtering systems.Image classification is one of the most critical aspects of machine learning and has been used in various digital curation, automated moderation, and artistic recognition applications. This paper studies various machine learning techniques for detecting artistic images from non-artistic ones. By incorporating all these traditional feature extractions with deep learning models, the paper aims at enhanced accuracy in classification and tackles many challenges, such as subjectivity, a wide variety of artistic styles, as well as inherent biases in the dataset. Such a model is expected to show robust performance and can be effectively applied in art galleries, content platforms, and automated filtering systems.