Image aesthetic style classification and region detection using Convolutional Neural Network
Date of Issue2017
School of Computer Science and Engineering
Convolutional Neural Network (CNN) becomes popular in recent years, especially in the field of image processing. This algorithm has been successfully applied on object image classification, object detection, video analysis and so on with good results. Due to good feature extraction performance of CNN, research on automatically aesthetic analysis of images by deep learning has started. However, previous work for image aesthetic analysis like  are mainly about image aesthetic rating or image aesthetic binary classification. Therefore, our project aims at learning the image aesthetic styles using CNN as well as generating the bounding box of region for corresponding styles. This project comprises of two main parts, which are image aesthetic style classification and image aesthetic style region detection. We firstly build the network based on  and train an image aesthetic style classification model on AVA Dataset  with some selected style classes after data cleaning. By using this pre-trained model, we then apply Faster R-CNN  algorithm on image aesthetic style region detection. This is implemented by firstly manually labeling image aesthetic style region in selected images in AVA Dataset, building corresponding Region Proposal Network and Fast R-CNN Network  based on RAPID Network  and training on these labeled images with pre-trained image aesthetic style classification model.
DRNTU::Engineering::Computer science and engineering
Final Year Project (FYP)
Nanyang Technological University