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A performance comparison of two versatile frequency transformation approach in texture image retrieval

This research compares retrieval performance between two frequency based feature against texture image retrieval. The aim is that to study the retrieval behavior by using two well-known frequency based features, which has a tiny differences of decomposition basis between DCT and DFT, this work come up with the assumption that different decomposing method might give different retrieval result. In this experiment, feature extraction performs straightforwardly by transforming grayscale global textural of each image into frequency domain without any pre-processing, then similarity measurement performs by Euclidean distance method. The result shows that DFT outperforms DCT for overall precision and recall.