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Volume 20 Issue 6
November  2005
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Abstract
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Shu-Qiang Jiang, Jun Du, Qing-Ming Huang, Tie-Jun Huang, Wen Gao. Visual Ontology Construction for Digitized Art Image Retrieval[J]. Journal of Computer Science and Technology, 2005, 20(6): 855-860.
Citation: Shu-Qiang Jiang, Jun Du, Qing-Ming Huang, Tie-Jun Huang, Wen Gao. Visual Ontology Construction for Digitized Art Image Retrieval[J]. Journal of Computer Science and Technology, 2005, 20(6): 855-860.
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Visual Ontology Construction for Digitized Art Image Retrieval

  • 1 Digital Media Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, P.R. China2 Research Center of Digital Media, Graduate School of the Chinese Academy of Sciences, Beijing 100049, P.R. China

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  • Received Date: July 19, 2004
  • Revised Date: May 14, 2005
  • Published Date: November 14, 2005
    Abstract
  • Current investigations on visual information retrieval are generally content-based methods. The significant difference between similarity in low-level features and similarity in high-level semantic meanings is still a major challenge in the area of image retrieval. In this work, a scheme for constructing visual ontology to retrieve art images is proposed. The proposed ontology describes images in various aspects, including type & style, objects and global perceptual effects. Concepts in the ontology could be automatically derived. Various art image classification methods areemployed based on low-level image features. Non-objective semantics are introduced, and how to express these semantics is given. The proposed ontology scheme could make users more naturally find visual informationand thus narrows the ``semantic gap''. Experimental implementation demonstrates its good potential for retrieving art images in a human-centered manner.
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  • [1]
    Flickner M, Sawhney H, Niblack W et al. Query by image and video content: The QBIC system. IEEE Computer, 1995, 28(9): 23--32.
    [2]
    Smith J R, Chang S-F. VisualSEEK: A fully automated content-based image query system. In Proc. ACM Multimedia, Nov. 1996, pp.87--98.
    [3]
    Carson C, Thomas M, Belongie S et al. Blobworld: A system for region-based image indexing and retrieval. In Proc. Visual Information Systems, June 1999.
    [4]
    Rui Y, Huang T S, Mehrotra S, Ortega M. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans. Circuits and Systems for Video Technology, 1998, 8(5): 644--655.
    [5]
    Burl M C, Weber M, Perona P. A probabilistic approach to object recognition using local photometry and global geometry. In Proc. European Conf. Computer Vision, June 1998, pp.628--641.
    [6]
    Wang J Z, Li J, Wiederhold G et al. Systems for screening objectionable images. Computer Comm., 1998, 21(15): 1355--1360.
    [7]
    Szummer M, Picard R W. Indoor-outdoor image classification. IEEE Int. Workshop on Content-Based Access of Image and Video Databases, in conjunction with ICCV'98 . Bombay, India, 1998, pp.42--51.
    [8]
    Rong Zhao, William I Grosky. Negotiating the semantic gap: From feature maps to semantic landscapes. Pattern Recognition, 2002, 35(3): 593--600.
    [9]
    Jia Li, James Z Wang. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Analysis and Machine Intelligence, 2003, 25(9): 1075--1088.
    [10]
    Kobus Barnard, Pinar Duygulu, Nando de Freitas et al. , David Forsyth, David Blei, Michael I Jordan. Matching words and pictures. J. Machine Learning Research, 2003, 3: 1107--1135.
    [11]
    Rodden K, Wood K. How do people manage their digital photographs ACM Conf. Human Factors in Computing Systems, April 2003, pp.409--416.
    [12]
    Thomas R Gruber. A translation approach to portable ontology specifications. Knowledge Acquisition, 1993, 5(2): 199--220.
    [13]
    Schreiber A T, Dubbeldam B et al. Ontology-based photo annotation. IEEE Intelligent Systems, May/June 2001, pp.66--74.
    [14]
    Hollink L, Schreiber A Th, Wielemaker J, Wielinga B. Semantic annotation of image collections. In Proc. the KCAP'03 Workshop on Knowledge Capture and Semantic Annotation, Florida, October 2003.
    [15]
    Bo Hu, Dasmahapatra S, Lewis P, Shadbolt N. Ontology-based medical image annotation with description logics. IEEE ICTAI'03, November 3--5, 2003, pp.77--82.
    [16]
    Hyv"onen E, Saarela S, Viljanen K. Ontology based image retrieval. In Proc. WWW 2003, Budapest, 2003, poster paper.
    [17]
    Soo Von-Wun, Lee Chen-Yu, Li Chung-Cheng et al. Automated semantic annotation and retrieval based on sharable ontology and case-based learning techniques. Joint Conf. Digital Libraries, 2003, pp.61--72.
    [18]
    Bob Wielinga, Guus Schreiber, Wielemaker J et al. From thesaurus to ontology. In Int. Conf. Knowledge Capture, Victoria, Canada, Oct. 2001, pp.194--201.
    [19]
    Peterson T. Introduction to the Art and Architecture Thesaurus. Oxford University Press, 1994. http://www.getty. edu/research/conducting\_research/vocabularies/aat/.
    [20]
    Hyv"onen E, Saarela S, Viljanen K. Intelligent image retrieval and browsing using semantic web techniques---A case study. In International SEPIA Conference at the Finnish Museum of Photography, Helsinki, September, 2003.
    [21]
    Mezaris V, Kompastsiaris I, Strintzis M G. An ontology approach to object-based image retrieval. In IEEE ICIP, 2003, pp.511--514.
    [22]
    Breen C, Khan L, Ponnusamy A, Wang L. Ontology-based image classification using neural networks. In Proc. SPIE Internet Multimedia Management Systems III, Boston, MA, July 2002, pp.198--208.
    [23]
    C Chen, A Del Bimbo, G Amato et al. Report of the DELOS-NSF working group on digital imagery for significant cultural and historical materials. DELOS-NSF Reports, Dec. 2002.
    [24]
    Jia Li, James Z Wang. Studying digital imagery of ancient paintings by mixtures of stochastic models. IEEE Trans. Image Processing, 2004, 12(3): 340--353.
    [25]
    Leykin A, Cutzu F, Hammoud R. Visual properties differentiating art from real scenes. Technical Report No. 565, Computer Science Department, Indiana University, 2002.
    [26]
    Shuqiang Jiang, Wen Gao, Weiqiang Wang. Classifying traditional Chinese painting images. In The 4th Int. Conf. Information, Communications & Signal Processing --4th IEEE Pacific-Rim Conf. Multimedia ( ICICS-PCM2003 ) , Singapore, Dec. 15--18, 2003, pp.1816--1820.
    [27]
    Carlo Colombo, Alberto Del Bimbo, Pietro Pala. Semantics in visual information retrieval. IEEE Multimedia, July--September 1999, 6(3): 38--53.
    [28]
    Pease A, Niles I, Li J. The suggested upper merged ontology: A large ontology for the semantic web and its applications. In Working Notes of the AAAI-2002 Workshop on Ontologies and the Semantic Web, Edmonton, Canada, July 28--August 1, 2002.
    [29]
    Shuqiang Jiang, Gao W, Huang T J. Categorizing traditional Chinese painting images into Gongbi and Xieyi. In IEEE PCM'2004, pp.1--8.
    [30]
    Lienhart R, Hartmann A. Classifying images on the web automatically. J. Electronic Imaging, Oct. 2002, 11(4): 445--454.
    [31]
    S Prabhakar, Hui Cheng, John C Handley et al. Picture-graphics color image classification. In IEEE ICIP, 2002, pp.785--788.
    [32]
    Qixiang Ye, Wen Gao, Wei Zeng. Color image segmentation using density-based clustering. In International Conference on Acoustic, Speech and Signal Processing, ICASSP2003, Hong Kong, Apr.6--10, 2003, pp.III/345--348.
    [33]
    Jun Miao, Hong Liu, Wen Gao et al. A system for human face and facial feature location. International Journal of Image and Graphics, July 2003, 3(3): 461--479.
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