Exploitation of meta knowledge for learning visual concepts
The paper proposes a content-based image retrieval system which can learn visual concepts and refine them incrementally with increased retrieval experiences captured over time. The approach consists of using fuzzy clustering for learning concepts in conjunction with statistical learning for computing "relevance" weights of features used to represent images in the database. As the clusters become relatively stable and correspond to human concept distribution, the system can yield fast retrievals with higher precision. The paper presents a discussion on problems such as the system mistakenly indentifying a concept, a large number of trials to achieve clustering, etc. Experiments on synthetic data and real image database demonstrate the efficacy of this approach.