Comparison of Color Classification Using Computer Vision and Deep Neural Network

Abstract

Research on artificial intelligence and  machine learning is currently ongoing and is focused on  real-world problems. Machine learning is used by  computers to make predictions based on the provided data  set or existing knowledge. The main goal of our project is  to use machine learning to categorize different colors while  separating CNN from computer vision. In this work, we  used supervised learning to categorize different hues using  a binary classification approach. Color detection is the  technique of identifying a color. In this scenario, humans  can recognize the hue and choose with ease. A computer,  however, cannot quickly recognize color. It is challenging  to get a computer to quickly detect the color. Given that,  we decide to pursue this initiative. Pandas, OpenCV, and  the Naive Bayes algorithm are all used in Python. Naive  Bayes classifiers are models that assign category labels to  issue occurrences that are represented as vectors of feature  values, where the category labels are selected from a finite  set. There isn't a single method for training these  classifiers; rather, there is a family of algorithms built on  the premise that, given a category variable, the value of one  feature is independent of the value of the other feature.  Open-Source Computer Vision Library OpenCV was  designed to be computationally effective and with a major  emphasis on real-time applications. specialized video  encoding for the cloud. Panda may be a platform that runs  in the cloud and provides infrastructure for encoding audio  and video.