A neural network approach for learning image similarity in adaptive CBIR

In this paper the adoption of neural network techniques is studied for the purpose of image retrieval. More specifically, we propose an adaptive retrieval system which incorporates learning capability into the image retrieval module where the network weights represent the adaptivity. This system can learn users' notions of similarity between images through the continual relevance feedback from the users. Accordingly it makes the proper adjustment to improve performance. This retrieval system has demonstrated its effectiveness in performance. It is confirmed by simulations conducted for applications such as texture retrieval and retrieval of the DCT compressed images.