NSU Research Contributions

Title : Differential Color Harmony: A robust approach for extracting Harmonic Color features and perceive aesthetics in a large dataset

Authors : Adnan Firoze, Shahreen Shahjahan Psyche, Tonmoay Deb, Tousif Osman, Rashedur M Rahman

Abstract : This research work introduces and describes a robust method for extracting harmonic color features (HCF) and verifies its validity by predicting visual aesthetics of a large image dataset against a large human survey on the same dataset. This work is a continuation of our previous research [1] where we demonstrated machine’s capability of understanding aesthetics with respect to the rule of thirds [2]. In this research, we have successfully devised a method of extracting HCF and trained a model that can perceive beauty with a root mean squared error of 1.115 +/- 0.196 on a scale of 0 to 5. In contrast to classic segmentation approaches, we have used HSV color scale gradients and differentials to extract the HCFs. Due to reduced computations, differential color harmony is quite suitable for big data and fast computing. We have used a large dataset of 5000 images from the standard MIRFLICKR [12] dataset and conducted a survey where participants measured the beauty of these images. We have used these data to train classifiers and regression models, and verified our approach by making the machine perceived beauty against human perceived beauty.

Journal :	Volume :	Year : 2018	Issue :
Pages :	City : Switzerland	Edition :	Editors : Randy Goebel, University of Alberta, Edmonton, Canada
Publisher : Springer Nature	ISBN :	Book : Lecture Notes in Artificial Intelligence	Chapter : Big Data and Cloud Computing
Proceeding Title :	Institution :	Issuer :	Number :