Oil Portrait Snapshot Classification on Mobile

In recent years, several art museums have developed smartphone applications as the e-guide in museums. However few of them provide the function of instant retrieval and identification for a painting snapshot taken by mobile. Therefore in this work we design and implement an oil portrait classification application on smartphone. The accuracy of recognition suffers greatly by aberration, blur, geometric deformation and shrinking due to the unprofessional quality of snapshots. Low-megapixel phone camera is another factor downgrading the classification performance. Carefully studying the nature of such photos, we adopts the SIPH algorithm (Scale-invariant feature transform based Image Perceptual Hashing)) to extract image features and generate image information digests. Instead of popular conventional Hamming method, we applied an effective method to calculate the perceptual distance. Testing results show that the proposed method conducts satisfying performance on robustness and discriminability in portrait snapshot identification and feature indexing.

[1]  Siddhant Jain,et al.  Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision , 2015, IET Image Process..

[2]  Franco Bartolini,et al.  Applications of image processing technologies to fine arts , 2003, SPIE Optical Metrology.

[3]  Andrew B. Watson,et al.  DCT quantization matrices visually optimized for individual images , 1993, Electronic Imaging.

[4]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[5]  Kwanghoon Sohn,et al.  3D Scene Reconstruction System with Hand-Held Stereo Cameras , 2007, 2007 3DTV Conference.

[6]  David G. Stork,et al.  Computer Vision and Computer Graphics Analysis of Paintings and Drawings: An Introduction to the Literature , 2009, CAIP.

[7]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[8]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[9]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

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

[11]  G. Székely,et al.  Definetti’s Theorem for Abstract Finite Exchangeable Sequences , 2006 .

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Jong Hyuk Park,et al.  Image hash generation method using hierarchical histogram , 2010, Multimedia Tools and Applications.

[14]  Joanna Gancarczyk,et al.  Data Mining Approach to Image Feature Extraction in Old Painting Restoration , 2013 .

[15]  K. Roy,et al.  ART based clustering of bag-of-features for image classification , 2012, 2012 5th International Congress on Image and Signal Processing.

[16]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[17]  Menzo Windhouwer,et al.  The role of high-level and low-level features in style-based retrieval and generation of multimedia presentations , 2001, New Rev. Hypermedia Multim..

[18]  Robin Hess.  An Open-Source SIFT Library , 2010 .

[19]  Licheng Jiao,et al.  Feature integration of EODH and Color-SIFT: Application to image retrieval based on codebook , 2014, Signal Process. Image Commun..

[20]  Alessandro Piva,et al.  Multispectral imaging of paintings , 2008, IEEE Signal Processing Magazine.

[21]  David Saunders,et al.  Ten years of art imaging research , 2002, Proc. IEEE.

[22]  Stefan Mordalski,et al.  An application of machine learning methods to structural interaction fingerprints--a case study of kinase inhibitors. , 2014, Bioorganic & medicinal chemistry letters.

[23]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .