A Deep Learning Perspective on Beauty Sentiment and Remembrance of Art


New research opportunities in the confluence of artificial intelligence and art are opening up as massive digitized fine art collections emerge and Deep Learning approaches perform well. We use convolutional neural networks (CNN) to predict scores related to three subjective aspects of human perception: aesthetic evaluation of the image, sentiment evoked by the image, and memorability of the image, in order to investigate the applicability of Deep Learning techniques in understanding art images beyond object recognition and classification. We assess multiple distinct CNN models trained on diverse natural image datasets for each topic and choose the best performing model based on qualitative results and comparisons with current subjective artwork judgments. In addition, we use a variety of decision tree-based Machine Learning models to assess the relative importance of various image attributes such as content, composition, and color in calculating image aesthetics, visual sentiment, and memorability scores. Our findings imply that aesthetics are influenced by both content and image illumination, with color vividness and harmony having a large influence on sentiment prediction and object emphasis having a strong impact on memorability. In addition, we examine the distribution of anticipated aesthetic, sentiment, and memorability scores in the context of art history, looking at different artistic styles, genres, artists, and centuries. The approach given here allows for new ways of examining fine art collections based on highly subjective qualities of art, as well as bridging the gap between traditional formal analysis and computational fine art analysis.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Revenue-Optimal Auction For Resource Allocation in Wireless Virtualization: A Deep Learning Approach ABSTRACT: Virtualization of wireless networks has emerged as an essential component of future cellular networks.
PROJECT TITLE : CNN-LSTM: Hybrid Deep Neural Network for Network Intrusion Detection System ABSTRACT: The importance of network security to our day-to-day interactions and networks cannot be overstated. The importance of having
PROJECT TITLE : Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network ABSTRACT: Because of the proliferation of wireless networks, wireless sensor networks
PROJECT TITLE : Traffic Signal Control Using End-to-End Off-Policy Deep Reinforcement Learning ABSTRACT: However, road intersections have historically been among the most significant traffic bottlenecks that have contributed
PROJECT TITLE : Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving ABSTRACT: The most important component of an autonomous driving system is the module that is responsible for pedestrian

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry