An Efficient Method for Landscape Image Classification and Matching Based on MPEG-7 Descriptors

In this thesis, an efficient approach for landscape image classification and matching system based on the MPEG-7 (Moving Picture Expert group) color and shape descriptor. Image classification is the task of deciding whether an image landscape or not. These classifications use the dominant color descriptor method for finding the dominant color in the image. In DCD we examine whole image pixel values. The pixel value contains Red, Green and Blue color values in the RGB color model. After calculating all pixels values we can say that which color has maximum value in image by performing some arithmetic operations. So this color is called dominant color and based on this color, we classify some landscape images. After DCD, we use shape and color structure for image matching with database images. Shape descriptor calculate some key points of image like number of objects in image, maximum object size and active pixels in binary image. Based on these key points, we match the database and store the resulted image. Now we apply CSD method on the resulted images from the shape descriptor and again perform matching to get the final result. .