| dc.description.abstract | Image aesthetic assessment is defined as to classify the image in aesthetically good images and aesthetically bad images. In era of digital media, video and images has impact more in human life. Image aesthetic assessment is also important part of digital media. In earlier research in this area was based on the use of photographic rules, generic image descriptors, or hand-crafted features. These photographic rule-based approaches have their limitations, such as it has approximation in applying rules in the implementation. It was observed that photographic rules such as color distribution, brightness, hue count, low contrast are not enough to judge the process of image aesthetics. Those hand-crafted features may be suited for some specific task but may not fully cover the feature space that represents the primary characteristic for image aesthetic task. In recent researches, deep learning-based approaches have achieved great success in image aesthetic assessment problem. In this thesis, we have implemented various multichannel CNN architectures to classify images in high aesthetic and low aesthetic images. We have also used some pre-processing techniques to row data like various crops, padding, and class activation maps (CAM) techniques. Along with that, we have also implemented various pre-trained deep learning models such as VGG19, InceptionV3, and Resnet50 on multi-channel CNN networks, and analyze their impact with multi-channel CNN networks. The experiments are implemented on the AVA dataset, which shows improvements in the image aesthetic assessment task over existing approaches. | |
