Content-based retrieval using fuzzy interactive and competitive neural networks

G. Senthilkumar; Jiankang Wu

doi:10.1117/12.227232

21 November 1995 Content-based retrieval using fuzzy interactive and competitive neural networks

G. Senthilkumar, Jiankang Wu

Proceedings Volume 2606, Digital Image Storage and Archiving Systems; (1995) https://doi.org/10.1117/12.227232
Event: Photonics East '95, 1995, Philadelphia, PA, United States

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.

Citation Download Citation

G. Senthilkumar and Jiankang Wu "Content-based retrieval using fuzzy interactive and competitive neural networks", Proc. SPIE 2606, Digital Image Storage and Archiving Systems, (21 November 1995); https://doi.org/10.1117/12.227232

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