an image retrieval system adaptable to user's interests by the use of relevance feedback via genetic algorithm
The emergence of multimedia technology and the rapid expansion of image sets on the internet have attracted a lot of research tools for effective retrieval of visual data. When working in the image retrieval context the main goal is to retrieve images which might be useful or relevant to the user based on features automatically extracted from the images. The proposal of this work is to integrate the information provided by the user into the decision procedure by the use of the relevance feedback mechanism. The relevance feedback technique used is based on genetic algorithms using a proposed order-based fitness function in order to adapt the user's image similarity criteria. Image similarity is expressed as a weighted integration of color, shape and texture features. The retrieval process itself is based on the Local Similarity Pattern, where the image areas are uniformly partitioned into regions, and the similarity between the images is measured by corresponding region similarities. The use of negative and positive weights for the features, into the genetic algorithm, allows one to express, in a continuous way, the concepts of relevance, irrelevance and undesirability in the similarity model used. Experiments in a database with 12750 images has shown that the integration of features through the proposed genetic algorithm into a relevance feedback mechanism provides good results in the image retrieval context.
image retrieval relevance feedback genetic algorithm