We present a two-stage mapping model (TSMM), which is intended to minimise the semantic gap for content-based image retrieval (CBIR) by reducing recognition errors during the image indexing stage. This model is composed of a feature extraction module based on our image segmentation and feature extraction algorithm, a colour and texture classification modules based on support vector machines (SVMs), and an inference module based on fuzzy logic to make final decisions as high level concepts from the colour and texture concepts. The experimental results show that the proposed method outperforms general approaches by using one single SVM classifier as direct mapping between the combined colour and texture feature vectors and high level concepts directly.
['Chih-Fong Tsai', 'Kenneth McGarry', 'John Tait']
Using neuro-fuzzy techniques based on a two-stage mapping model for concept-based image database indexing