Content-based retrieval using fuzzy interactive and competitive neural networks

Abstract

It is a challenging issue to provide a user-friendly means to retrieve information from a very large database when the user cannot clearly define what the information must be. To find images which are most relevant to the given description, we proposed a Fuzzy Interactive Activation and Competitive (FIAC) neural network model. There are two layers of units in a FIAC: concept layer and example layer. The example layer stores data and feature measures of images while the concept layer represents the attributes of the images. The links between the concept units and the example units are fully connected and bidirectional-directional with different link function for each direction. To ensure best matches, inhibitory links are created among example units and among conceptual units in the same pool. When a query is defined by specifying truth values for a set of concept units, the neural network will run for a given number of cycles and the images relevant to those fuzzy subsets will have the maximum activation. The FIAC neural net for retrieval has been tested for face images, and produced very promising results.