Improving Image Retrieval by Fuzzy C-Means Initialized by Fixed Threshold Clustering: case studies relating to a color temperature histogram and a color histogram

摘要

Fuzzy C-Means (FCM) algorithm is one of the well-known unsupervised clustering techniques. Such an algorithm can be used for unsupervised image clustering. The different initializations cause different evolutions of the algorithm. Random initializations may lead to improper convergence. This paper proposes FCM algorithm initialized by fixed threshold clustering. The purpose of the algorithm is to retrieve from the database the color JPEG images. Two case studies regard to index or represent the color images by either using color temperature histogram or color histogram vectors. The clustering process produces from such an image index the information, which is a degree of membership for each image. This information would be stored in a database. This paper shows that for both two cases, FCM algorithm initialized by fixed threshold clustering gives more accurate results than FCM with random initialization does.