Performance on Image Segmentation Resulting In Canny and MoG

Images are analyzed with edge and color values. Pixel information is used in the color property extraction. Texture and contrast are pixel based features. Shape or edge features are used to represent images. The images are assigned with their category values. The image features are used in the classification process. Classification techniques are used to assign labels to the images. Color constancy methods are largely dependent on the distribution of colors and color edges in an image. Natural image statistics and scene semantics are used in the color consistency methods. Color contrast and texture values are used in natural image statistics model. The scene semantics model uses the edge parameter values. Classification techniques are used to learn color consistency in images. The mixture of Gaussians (MoG) classifier is enhanced with feature integration model The proposed system is designed to improve the classification accuracy. The natural image statistics and scene semantic features are combined in the classification process. Integrated feature weight is assigned for the images to perform the learning process. The MoG algorithm is enhanced with combined feature weight model. The combined feature weight is used I segmentation the class assignment process. The image similarity is estimated with the feature weight values.