Fuzzy C-Means Clustering For Content Based Image Retrieval System

Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. The present Content Based Image Retrieval system is to describe the solution to the problem of retrieving the query image from the large image database using fuzzy C-Means clustering method. The CBIR can use the primitive features of an image such as texture, color, orientation and shape. These features are extracted and used as the basis for a similarity check between images stored in the database. In the proposed approach the color feature is used and uses the fuzzy c-means clustering algorithm. The database consists of 8 bit bmp format images of 256×256 size. The Hue Saturation Value color space is used. The color Histogram of each image in the database and the query image is obtained & then Median Filtering is applied to reduce the noise. The Fuzzy C-Means Clustering can used to obtain the more features of the images and to improve retrieval efficiency. The similarity between the query image and the images in the database is done using Quadratic Distance approach and the minimum distance image is retrieved from the database. The final result is obtained, that utilizes the features of the images as the basis for comparison and retrieval using different Matlab functions.