The main focus of this paper is to present a methodology for optimizing relevance indentification in content-based image retrieval (CBIR) system through the principle of feature weight detection. The purpose of relevance identification is to find a collection of images that are statistically similar to, or match with, an original query image within a large visual database. The novelty of this scheme is two-fold: using a base-10 Genetic Algorithm method to accurately determine the contribution of individual feature vectors for a successful retrieval in the so-called feature weight detection process, and defining a new unsupervised learning algorithm, the Directed self-organizing tree map (DSOTM), for the purpose of classification in the automatic relevance identification module of the search engine. Comprehensive experiments demonstrate feasibility of the proposed methodology.