SIFT Applied to Perceptual Zoning for Trademark Retrieval

The paper contributes to the CBIR systems applied to trademark retrieval. The proposed model uses Scale Invariant Feature Transform (SIFT) and includes aspects from visual perception of the shapes, by means of feature extractor associated to a non-symmetrical perceptual zoning mechanism based on the Principles of Gestalt. We carried out experiments using four different zonings strategies for matching and retrieval tasks. The proposed method achieved the normalized recall (Rn) equal to 0.84. Experiments show that the non-symmetrical zoning could be considered as a tool to build more reliable trademark retrieval systems.