Content-Based Image Retrieval System Based on Self Organizing Map, Fuzzy Color Histogram and Subtractive Fuzzy Clustering

A novel system with high level of retrieval accuracy has been presented in this paper. Color as one of the most important discriminators in CBIR (content-based image retrieval) is utilized through calculating some of the primitive color features. The indexing of image database is performed with SOM (self-organizing map) which identified the BMU's (best matching units). Subsequently, Fuzzy Color Histogram (FCH) and subtractive fuzzy clustering algorithms have been utilized to identify the cluster for which the query image is belonging. Furthermore, the paper presents an enhanced edge detection algorithm to remove unwanted pixels and to solidify objects within images which ease similarity measures based on extracted shape features. The proposed approach overcomes the computational complexity of applying bin-to-bin comparison as a multi dimensional feature vectors in the original color histogram approach and improves the retrieval accuracy based on shape as compared with the most dominant approaches in this filed of study.