Remove Unimportant Features from True Colored Images Using the Segmentation Technique

Remove Unimportant Features from True Colored Images Using the Segmentation Technique

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In this work a new approach was built LIP SHIMMER PLUM to apply k-means algorithm on true colored images (24bit images) which are usually treated by researchers as three image (RGB) that are classified to 15 class maximum only.We find the true image as 24 bit and classify it to more than 50 classes.As we know k-means algorithm classify images to many independent classes or features and we could increase the class number therefore we could remove the classes or features that have minimum number of pixels which are considered unimportant features and reconstruct the images.Correlation factor and Signal to Noise Ratio were used to measure the work and the results seems that by increasing the image resolution the effect of removing minimum features is decreased.The CSharp (Visual Studio 2008) programming language was used to build the algorithms which LIPSTICK MAGENTA RUSH are able to allocate huge matrices in high execution time.