Pratyush Kumar, Kumar Abhishek, Rushikesh Shukla, Manali Sarkar, G P Kaushal, Pankaj Gharde, Urmil Shah, Suhrud Panchawagh, Shabarini Srikumar
Prevalence and Assessment of Factors Associated With Malnutrition in Children Residing in Slums of Mumbai: A Cross-Sectional Study
Cureus
interpretation colors visualizations facilitated assignments colors concepts visualizations match humans expectations implying colors interpreted semantic manner however manually creating dataset suitable associations colors concepts use visualizations costly associations would collected humans large variety concepts address challenge collecting data introduce method extract colorconcept associations automatically set concept images stateoftheart method extracts associations data supervised learning developed selfsupervised method based colorization require preparation ground truth colorconcept associations key insight set images concept sufficient learning colorconcept associations since humans also learn associate colors concepts mainly past visual input thus propose use automatic colorization method extract statistical models colorconcept associations appear concept images specifically take colorization model pretrained imagenet finetune set images associated given concept predict pixelwise probability distributions lab color space images convert predicted probability distributions color ratings given color library aggregate images concept obtain final colorconcept associations evaluate method using four different evaluation metrics via user study experiments show although stateoftheart method based supervised learning userprovided ratings effective capturing relative associations selfsupervised method obtains overall better results according metrics like earth movers distance emd entropy difference ed closer human perception color distributions