Content based image retrieval using a neuro-fuzzy technique

In this paper, we propose a neuro-fuzzy technique for content based image retrieval. The technique is based on fuzzy interpretation of natural language, neural network learning and searching algorithms. Firstly, fuzzy logic is developed to interpret natural expressions such as mostly, many and few. Secondly, a neural network is designed to learn the meaning of mostly red, many red and few red. The neural network is independent to the database used, which avoids re-training of the neural network. Finally, a binary search algorithm is used to match and display neural network´s output and images from database. The proposed technique is very unique and the originality of this research is not only based on hybrid approach to content based image retrieval but also on the new idea of training neural networks on queries. One of the most unique aspects of this research is that neural network is designed to learn queries and not databases. The technique can be used for any real-world online database. The technique has been implemented using CGI scripts and C programming language. Experimental results demonstrate the success of the new approach