International Journal of Engineering and Manufacturing(IJEM)
ISSN: 2305-3631 (Print), ISSN: 2306-5982 (Online)
Published By: MECS Press
IJEM Vol.7, No.5, Sep. 2017
Model Based Approach for Identification of Relevant Images from Ancient Paintings
Full Text (PDF, 455KB), PP.39-47
In this paper an attempt is made to retrieve the relevant paintings based on the approach of the artist using Generalized Bivariate Laplacian Mixture Model (GBLMM). This article helps in understanding the outline of assorted artists and help as a means to categorize a scrupulous painting based on the style or the text ingrained within the images. To profile the artist style GBLMM is used. The projected model helps to discriminate the strokes of the artists and lend a hand in the classification of paintings. The proposed model is implemented using high resolution Chinese painting images.
Cite This Paper
G.G.Naidu, Y.Srinivas,"Model Based Approach for Identification of Relevant Images from Ancient Paintings", International Journal of Engineering and Manufacturing(IJEM), Vol.7, No.5, pp.39-47, 2017.DOI: 10.5815/ijem.2017.05.04
C.-C. Chen, A. Del Bimbo, G. Amato, N. Boujemaa, P. outhemy, J.Kittler, I. Pitas, A. Smeulders, K. Alexander, K. Kiernan, C.-S. Li, H.Wactlar, and J. Z. Wang, “Report of the DELOS-NSF Working Group on Digital Imagery for Significant Cultural and Historical Materials,” DELOS-NSF Rep., Dec. 2002
C. Bouman and B. Liu, “Multiple resolution segmentation of textured images,” IEEE Trans. Pattern Anal. Machine Intell., vol. 13, no. 2, pp.99–113, 1991
S. Ravela and R. Manmatha, “Image retrieval by appearance,” in Proc.SIGIR, Philadelphia, PA, July 1997, pp. 278–285.
A. P. Dhawan, Y. Chitre, C. Kaiser-Bonasso, and M. oskowitz, “Analysis of mammographic microcalcifications using gray-level image structure features,” IEEE Trans. Med. Imag., vol. 15, pp. 246–259, June 1996
N. Chaddha, R. Sharma, A. Agrawal, and A. Gupta, “Text segmentation in mixed-mode images,” in Proc. Asilomar Conf. Signals, Systems, Computers, vol. 2, Nov. 1994, pp. 1356–1361
R.S. Arora, A. Elgammal, Towards automated classification of fine-art painting style: a comparative study, in: ICPR, 2012, pp. 3541–3544.
Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, Greedy layer-wise training of deep networks, in: NIPS, 2006, pp. 153–160.
M. Bressan, C. Cifarelli, F. Perronnin, An analysis of the relationship between painters based on their work, in: ICIP, 2008, pp. 113–116.
C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (2011) 27:1–27:27.
R. Condorovici, C. Florea, R. Vranceanu, C. Vertan, Perceptually-inspired artistic genre identification system in digitized painting collections, Image Analysis, LNCS, vol. 7944, Springer, 2013. 687–696.
T. Fawcett, An introduction to ROC analysis, Pattern Recogn. Lett. 27 (2006) 861–874.
V. Ferrari, L. Fevrier, F. Jurie, C. Schmid, Groups of adjacent contour segments for object detection, IEEE TPAMI 30 (2008) 36–51.
D.J. Graham, J.M. Hughes, H. Leder, D.N. Rockmore, Statistics, vision, and the analysis of artistic style, Wiley Interdisciplinary Rev. Comput. Stat. 4 (2012) 115–123.
J. Li and J.Z. Wang, “Studying digital imagery of ancient paintings by mixtures of stochastic models,” IEEE Trans. Image Processing, vol. 13, no. 3, pp. 340–353, Mar.2004.
E.Hendriks and M. Geldof, “Van Gogh’s Antwerp and Paris picture supports (1885–1888): Reconstructing choices,” Art Matters: Netherlands Technical Studies in Art History, vol. 2, pp. 39–75, 2005.
E. Hendriks and L. van Tilborgh, “New views on Van Gogh’s development in Antwerp and Paris: An integrated art historical and technical study of his paintings in the Van Gogh Museum,” Ph.D. dissertation, Faculty of Humanities, Univ. Amsterdam, vol. 1, p. 132, and vol. 2, p. 62, cat. 72, Nov.2006.
C.R. Johnson, Jr., Ed., Proc. 1st Int. Workshop Image Processing for Artist Identification. Amsterdam, The Netherlands, May 2007 [Online]. Available: http://digitalpaintinganalysis.org/literature/ProceedingsIA4AI-1.pdf
Thai V. Hoang, S. Tabbone(2010),“Text Extraction From Graphical Document Images Using Sparse Representation “in Proc. Das, pp 143–150.
Y. Zhan, W. Wang, W. Gao (2006), “A Robust Split-And-Merge Text Segmentation Approach For Images”, International Conference On at tern Recognition,06(2): pp 1002-1005.
M.M. Van Dantzig, Vincent A New Method of Identifying the Artist and His Work and of Unmasking the Forger and His Products. Amsterdam, The Netherlands: Keesing, 1952, pp. 24–25.
Zhang D, Lu G. A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval. J Visual Common Image Representation 2003; 14:41–60.
F.Cheng, H.Zhang, M.Sun, D.Yuan, “Cross-trees, edges and super-pixel priors-based cost aggregation for stereo matching”, in Pattern Recognition 48 (2015) 2269-2278.
K. Andreas, M. Sormann, K. Karner, “Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure”, in: International Conference on Pattern Recognition, vol. 3, 2006, pp. 15–18.
F. Cheng, H.Zhang, D.Yuan, M.Sun, “Stereo matching by using the global edge constraint”, in Neurocomputing (2013).
X.Wang, H.Wang, Y.Su, “Accurate belief propogation with parametric and non-parametric measures for stereo-matching”, in Optik 126(2015) 545-550.
F.Da, F.He, Z.Chen, “Stereo Matching based on dissimilar intensity support and belief propogation”, in J Math Imaging Vis (2013) 47:27–34.
F. Tombari, S. Mattoccia, L. Di Stefano, E. Addimanda, “Classification and evaluation of cost aggregation methods for stereo correspondence”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
H.Han, X.Han, F.Yang, “An improved gradient-based dense stereo correspondence algorithm using guided filter”,in Optik125 (2014) ,pp.115– 120.
F.Tombari, S.Mattoccia, L.Stefano Di, “Segmentation-Based Adaptive Support for Accurate Stereo Correspondence”, in PSIVT 2007, LNCS 4872, pp. 427–438, 2007.
O. Veksler, “Stereo correspondence by dynamic programming on a tree”, in: IEEE International Conference on Computer Vision and Pattern Recognition, 2005, pp. 384–390.
R.Elias, “Sparse view Stereo Matching”, in Pattern Recognition Letters 28 (2007) 1667–1678.
S.Yoon, S.K.Park, Y.K.Kwak, “Fast correlation-based stereo matching with the reduction of systematic errors”, Pattern Recognition Letters 26(2005), pp.2221-2231.
K.Yoon, I.So.Kweon, “Adaptive Support-weight approach for correspondence search”, IEEE Transactions on Pattern Analysis and Machine Intelligence(2006),vol.28,no.4.
P.H.S.Torra, A.Criminisi, “Dense stereo using pivoted dynamic programming”, Image and Vision Computing 22(2004), pp.795-806.
M.Bleyer, M.Gelautz, “ A layered stereo matching algorithm using image segmentation and global visibility constraints”, ISPRS Journal of Photogrammetry & Remote Sensing 59(2005),pp.128-150
O.Veksler, “Extracting dense features for visual correspondence with graph cuts”, Proceedings of the IEEE Computer Society Conference on Computer vision and Pattern Recognition (2003).
M.Gong, Y.H.Yang, “Fast stereo matching using reliability based dynamic programming and consistency constraints”, Proceedings of ninth IEEE International Conference on computer vision (2003).
C. L. Zitnick and T. Kanade, "A Cooperative Algorithm for Stereo Matching and Occlusion Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no.7, 2000.
J. Sun, H. Y. Shum and N. N. Zheng, “Stereo Matching Using Belief Propagation,”Proc. European Conf. Computer Vision (2002), pp. 510-524.
V. Komolgorov and R. Zabih, “Computing Visual Correspondence with Occlusions using Graph Cuts”, Proc. Int?l Conf. Computer Vision (2002).
Y. Boykov, O. Veksler and R. Zabih, “Fast Approximate Energy Minimization via GraphCuts”, IEEE Trans. Pattern Analysis and Machine Intelligence (2001), vol. 23, no. 11, pp. 1222-1239.
S. Birchfield and C. Tomasi, “Depth discontinuities by pixel-to-pixel stereo”, In ICCV (1998), pages 1073–1080.
Z. Yongqin, C.Hui, W. Ling, X.Yongjun, H.Haibo, “Color Image Segmentation Using Level Set Method With Initialization Mask in Multiple Color Spaces”, I.J. Engineering and Manufacturing, 2011, 4, 70-76.
Ramandeep Kaur, Kamaljit Kaur, “Study of Image Enhancement Techniques in Image Processing: A Review”, I.J. Engineering and Manufacturing, 2016, 6, 38-50.