Image classification using Gradient-Based Fuzzy c-Means with Divergence Measure

This paper proposes a novel classification method for image retrieval using gradient-based fuzzy c-means with divergence measure (GBFCM(DM)). GBFCM(DM) is a neural network-based algorithm that utilizes the Divergence Measure to exploit the statistical nature of the image data and thereby improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed classification algorithm outperforms conventional algorithms such as the traditional self-organizing map (SOM) and fuzzy c-means (FCM) by 27% - 28.5% in terms of accuracy.