Image retrieval system capable of learning the user's sensibility using neural networks

With the advent of the multimedia era, the need to retrieve the image that a user wants from a lot of images is an important issue. In this paper, we propose an interactive image retrieval system which employs backpropagation neural networks using the words that represent the user's sensibility, in order to deal with the user's ambiguous queries. When an user inputs the words, this system sets the synapse of the network which represents both the user and the words and displays candidate images according to the output values of the neural network. The user evaluates the similarity of the image that he/she wants to get until the system displays the optimal images and produces the set of teach signals according to the user's evaluation. After training the network, the system displays new candidate images. The inputs of the neural network are image features which have one-to-one correspondence with images in the databases. We implemented this system on Sun SPARC station, and show that the system could improve the candidate images each time an user evaluate them.