Image Semantic Classification by Using SVM
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    Abstract:

    There exists an enormous gap between low-level visual feature and high-level semantic information, and the accuracy of content-based image classification and retrieval depends greatly on the description of low-level visual features. Taking this into consideration, a novel texture and edge descriptor is proposed in this paper, which can be represented with a histogram. Furthermore, with the incorporation of the color, texture and edge histograms seamlessly, the images are grouped into semantic classes using a support vector machine (SVM). Experiment results show that the combination descriptor is more discriminative than other feature descriptors such as Gabor texture.

    Reference
    [1]Zhang J, Hsu W, Lee ML. Image mining: Issues, frameworks and techniques. In: Proceedings of the 2nd International Workshop on Multimedia Data Mining. San Francisco, 2001. 13~20.
    [2]Jain AK, Murty MN, Flynn PJ. Data clustering: A review. ACM Computing Survey, 1999,31(3):264~323.
    [3]Smith JR, Chang SF. Multi-Stage classification of images from features and related text. In: Proceedings of the 4th Europe EDLOS Workshop. San Miniato, 1997. http://www.ctr.columbia.edu/~jrsmith/html/pubs/DELOS97/delosfin/delosfin.html.
    [4]Bruzzone L, Prieto DF. Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(2):456~460.
    [5]Zaiane OR. Resource and knowledge discovery from the Internet and multimedia repositories [Ph.D. Thesis]. Burnaby, Simon Fraser University, 1999.
    [6]Vailaya A, Figueiredo AT, Jain AK, Zhang HJ. Image classification for content-based indexing. IEEE Transactions on Image Processing, 2001,10(1):117~130.
    [7]Li J, Wang JZ, Wiederhold G. Classification of textured and non-textured images using region segmentation. In: Proceedings of the 7th International Conference on Image Processing. Vancouver, 2000. 754~757.
    [8]Chapelle O, Haffner P, Vapnik V. SVMs for histogram-based image classification. IEEE Transactions on Neural Network, 1999,9. http://citeseer.nj.nec.com/chapelle99svms.html
    [9]Smith JR, Chang SF. Tools and techniques for color image retrieval. In: SPIE Proceedings of the Storage & Retrieval for Image and Video Database IV. 1996. 426~437.
    [10]Ortega M, Rui Y, Chakrabarti K, Warshavsky A, Mehrotra S, Huang TS. Supporting ranked boolean similarity queries in MARS. IEEE Transactions on Knowledge Data Engineering, 1998,10(6):905~925.
    [11]Karkanis S, Galousi K, Maroulis D. Classification of endoscopic images based on texture spectrum. In: Workshop on Machine Learning in Medical Applications. Chania, 1999. 63~69.
    [12]Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996,18(8):837~842.
    [13]Rosin PL, West GAW. Nonparametric segmentation of curves into various representations. IEEE Transactions on PAMI, 1995, 17(12):1140~1152.
    [14]Park DK, Jeon YS, Won CS. Efficient use of local edge histogram descriptor. In: Proceedings of the ACM Workshops on Multimedia. Los Angeles, 2000. 51~54.
    [15]Zhang L. Research of the cooperation between human and computer in content-based image retrieval [Ph.D. Thesis]. Beijing: Tsinghua University, 2001 (in Chinese).
    [16]Cao LH, Liu W, Li GH. Research and implementation of an image retrieval algorithm based on multiple dominant colors. Journal of Computer Research & Development, 1999,36(1):96~100 (in Chinese with English abstract).
    [17]Pengyu Hong, Qi Tian, et al. Incorporate support vector machines to content-based image retrieval with relevant feedback. In: Proceedings of the International Conference Image Processing. 2000. 750~753.
    [18]Vapnik VN, translated by Zhang XG. The Nature of Statistical Learning Theory. Beijing: Tsinghua University Press, 2000 (in Chinese).
    [19]张磊.基于内容的图像检索中人机协同问题的研究[博士学位论文].北京:清华大学计算机科学与技术系,2001.
    [20]曹莉华,柳伟,李国辉.基于多种主色调的图像检索算法研究与实现.计算机研究与发展,1999,36(1):696~100.
    [21]Vapnik VN著,张学工译.统计学习理论的本质.北京:清华大学出版社,2000.
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万华林,Morshed U. Chowdhury.基于支持向量机的图像语义分类.软件学报,2003,14(11):1891-1899

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  • Received:September 28,2002
  • Revised:December 04,2002
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