Image Retrieval Technique Using Labeling Algorithm and Hippocampal Learning

Recently there have been many efforts to support searching and browsing based on the visual content of image and multimedia data. In this paper, we propose a user friendly object-based image retrieval system using labeling algorithm and hippocampal learning. Most existing ways of queries in content-based image retrieval rely on query by example or query by sketch. But these methods of queries are not adequate to needs of people's various queries because they are not easy for people to use and restrict. We propose a method of automatic color object extraction using CSB tree map (color and spatial based binary tree map). Extracted objects were transformed to bit stream representing information such as color, size and location by region labeling algorithm and they are learned by the hippocampal neural network using the interplay between cortex and hippocampus. The cells of exciting at peculiar features in brain generate the special sign when people recognize some patterns. The hippocampus playing these such role in brain transfers information from short-term memory to long term-memory. The existing neural networks treat each attribute of features evenly. Proposed hippocampal neural network makes an adaptive fast content-based image retrieval system using excitatory learning method that forwards important features to long-term memories and inhibitory learning method that forwards unimportant features to short-term memories controlled by impression