Visual Saliency for the Visualization of Digital Paintings

Over the last 15 years, several applications have been developed for digital cultural heritage in the image processing and particularly in the area of digital painting. In order to help preserve cultural heritage, this chapter proposes several applications for digital paintings such as restoration, authentication, style analysis and visualization. For the visualization of digital paintings we present specific methods to visualize digital paintings based on visual saliency and in particular we propose an automatic digital painting visualization method based on visual saliency. The proposed system consists of extracting regions of interest (ROI) from a digital painting to characterize them. These close-ups are then animated on the basis of the paintings characteristics and the artist’s or designer’s aim. In order to obtain interesting results from short video clips, we developed a visual saliency map-based method. The experimental results show the efficiency of our approach and an evaluation based on a Mean Opinion Score validates our proposed method.

[1]  Soo-Chang Pei,et al.  Efficient implementation of quaternion Fourier transform, convolution, and correlation by 2-D complex FFT , 2001, IEEE Trans. Signal Process..

[2]  James Ze Wang,et al.  Studying digital imagery of ancient paintings by mixtures of stochastic models , 2004, IEEE Transactions on Image Processing.

[3]  Mauro Barni,et al.  ArtShop: an art-oriented image-processing tool for cultural heritage applications , 2003, Comput. Animat. Virtual Worlds.

[4]  R. Quiroga,et al.  How Do We See Art: An Eye-Tracker Study , 2011, Front. Hum. Neurosci..

[5]  Shannon M. Hughes,et al.  Stylistic analysis of paintings usingwavelets and machine learning , 2009, 2009 17th European Signal Processing Conference.

[6]  Pietro Perona,et al.  Overcomplete steerable pyramid filters and rotation invariance , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Liming Zhang,et al.  A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression , 2010, IEEE Transactions on Image Processing.

[8]  Bärbel Mertsching,et al.  Fast and Robust Generation of Feature Maps for Region-Based Visual Attention , 2008, IEEE Transactions on Image Processing.

[9]  F. Stanco,et al.  Digital Imaging for Cultural Heritage Preservation: Analysis, Restoration, and Reconstruction of Ancient Artworks , 2011 .

[10]  C. Koch,et al.  Models of bottom-up and top-down visual attention , 2000 .

[11]  Ioannis Pitas,et al.  Digital image processing techniques for the detection and removal of cracks in digitized paintings , 2006, IEEE Transactions on Image Processing.

[12]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[13]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[14]  J. Wolfe,et al.  What attributes guide the deployment of visual attention and how do they do it? , 2004, Nature Reviews Neuroscience.

[15]  Ulrich Ansorge,et al.  Salience in Paintings: Bottom-Up Influences on Eye Fixations , 2011, Cognitive Computation.

[16]  Patrick Le Callet,et al.  Computational modeling of artistic intention: Quantify lighting surprise for painting analysis , 2016, 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX).

[17]  William Puech,et al.  Visualization framework of digital paintings based on visual saliency for cultural heritage , 2015, Multimedia Tools and Applications.

[18]  Soo-Chang Pei,et al.  Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis , 2004, IEEE Transactions on Image Processing.

[19]  Christof Koch,et al.  Feature combination strategies for saliency-based visual attention systems , 2001, J. Electronic Imaging.

[20]  Jia Li,et al.  Image processing for artist identification , 2008, IEEE Signal Processing Magazine.

[21]  Aleksandra Pizurica,et al.  Spatiogram features to characterize pearls in paintings , 2011, 2011 18th IEEE International Conference on Image Processing.

[22]  G. U. Ebuh,et al.  Modified Wilcoxon Signed-Rank Test , 2012 .

[23]  Bo Wu,et al.  A unified framework for spatiotemporal salient region detection , 2013, EURASIP J. Image Video Process..

[24]  Timothy K. Shih,et al.  Multi-layer inpainting on Chinese artwork , 2004, ICME.

[25]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[26]  William Puech,et al.  TSAR: SECURE TRANSFER OF HIGH RESOLUTION ART IMAGES , 2008 .

[27]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[28]  Jon Y. Hardeberg,et al.  Evaluation of Digital Inpainting Quality in the Context of Artwork Restoration , 2012, ECCV Workshops.

[29]  C. Vertan,et al.  SALIENCY MAP RETRIEVAL FOR ARTISTIC PAINTINGS INSPIRED FROM HUMAN UNDERSTANDING , 2011 .

[30]  Eric O. Postma,et al.  Authentic: Computerized Brushstroke Analysis , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[31]  Michael Felsberg,et al.  Painting-91: a large scale database for computational painting categorization , 2014, Machine Vision and Applications.

[32]  Ann McNamara,et al.  Directing gaze in narrative art , 2012, SAP.

[33]  King Ngi Ngan,et al.  Saliency detection using joint spatial-color constraint and multi-scale segmentation , 2013, J. Vis. Commun. Image Represent..