Learning similarity space

We suggest a method to adapt an image retrieval system into a configurable one. Basically, the original feature space of a content-based retrieval system is nonlinearly transformed into a new space, where the distance between the feature vectors is adjusted by learning. The transformation is realized by an artificial neural network architecture. A cost function is defined for learning and optimized by the simulated annealing method. Experiments are done on the texture image retrieval system, which use Gabor filter features. The results indicate that configured image retrieval system is significantly better than the original system.