Segmentation and classification of fine art paintings
Zuzana Berger Haladová, Elena Šikudová · 2010
Since the development of the first text-based image search on the internet, the area of image retrieval has come a long way to sophisticated content based image retrieval systems. On the other hand, the semantic gap causes that it is still not possible to create a system which can correctly identify any object in the image. However, this paper proposes a solution for classifying the one sort of objects- paintings. This approach includes segmentation of the painting from the image, creation of the descriptor file from the segmented painting, and classification of the painting by matching its descriptor file to the created database of descriptor files of original paintings. The segmentation of the painting is achieved with 3 preprocessing steps, followed by adjusted Hough transformation. For the estimation of key points and creation of the descriptor file, the SIFT (Scalable Invariant Feature Transform) or the SURF(Speeded Up Robust Features) technique is used. The performance of both techniques is validated within the paper. The solution proposed in this paper was tested on the database of 100 Rembrandt Harmenszoon van Rijn’s paintings.