A novel and more effective region-based image retrieval strategy is presented based on semantic ontology. An unsupervised segmentation algorithm splits images into regions that are subsequently used as basis by the ontology-based strategy. The approach comprises three stages, namely automatic region generation, categorization and ontology construction. When receiving a query for a specific object, the search engine will, in addition to conventionally matched images, also find candidates through the semantic ontology using low level features. The proposed approach can thus find a richer set of related candidate images than traditional image retrieval approaches. This strategy is particularly useful for vague queries encountered by inexperienced users that are not trained in searching for images by the means of low-level features. The experimental results demonstrate the effectiveness of the proposed approach.