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International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN
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Volume Issue
Volume-9,Issue-9  ( Sep, 2021 )
Paper Title
Color Detection using Machine Learning Unsupervised Algorithm
Author Name
S.Ilakiya, J.Shruthi, Ms. Deivarani
Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India
This paper presents an original low level system for color image segmentation considering the Hue-Saturation- Value (HSV) color space. We will be working with colors and you will get to learn about many concepts throughout this project. The Human eyes and brains work together to translate light into color. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the color. Since childhood, we have mapped certain lights with their color names. We will be using the somewhat same strategy in Image Color Detection using python. The HSV model has 3 components that is hue, saturation, and value. (Hue) is the color portion of the model, expressed as a number from 0 to 360 degrees, saturation appears as a range from 0 to 1, where 0 is gray and, 1 is a primary color. Value works in conjunction with saturation and describes the brightness or intensity of the color, from 0 to 100 percent, where 0 is completely black, and 100 is the brightest and reveals the most color. We will be using a dataset that contains RGB values with their corresponding names. CSV file includes 865 colors names along with their RGB and hex values. An interactive GUI tool is developed in Python and implemented to extract only the foreground from an image by adjusting the values for H (Hue), S (Saturation) and V (Value). The input is an RGB image and the output will be a segmented color image.
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