A solely excitatory oscillator network (SEON) is proposed for color image segmentation. SEON utilizes its parallel nature to reliably segment images in parallel. The segmentation speed does not decrease in a very large network. Using NBS distance, SEON effectively segments color images in term of human perceptual similarity. Our model obtains an average segmentation rate of over 98.5%. It detects vague boundaries very efficiently. Experiments show that it segments faster and more accurately than other contemporary segmentation methods. The improvement in speed is more significant for large images.