Automated generation of hierarchic image database with hybrid method of ontology and GMM-based image clustering

In the field of computer vision, “generic object recognition” is one of the most important topics. Generic object recognition needs three types research: feature extraction, pattern recognition, and database preparation. This paper targets at database preparation, and proposes a method to automatically generate hierarchic image database. The proposed method considers both object semantic and visual features in images. In the proposed method, semantic is covered by ontology framework, and visual similarity is covered by images clustering based on Gaussian Mixture Model. The image databases generated by the proposed method covered over 4,800 concepts (where 152 concepts have more than 100 images) and its structure was hierarchic. Through the subjective evaluation experiments, whether images in the database were correctly mapped or not was examined. The results of the evaluation experiments showed over 84% precision in average. It is suggested that the generated image database was sufficiently practicable as learning database for generic object recognition.