Principal Component Linear Coding for Visual Words
Author:
Affiliation:

  • Article
  • | |
  • Metrics
  • |
  • Reference [10]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    By means of significant test and co-linearity analysis, this paper proposes principal component linear encoding which selects the K-nearest neighbor visual word with the strongest linear correlation. The multiple linear regression method based on principal component is used to solve weak and instable coding caused by the visual words' co-linearity problem, improving the accuracy of the visual object classification effectively. Recognizing that the scarcity of the image quantify plays an important roles in the classification accuracy, the study analyzes the scarcity of the quantitative results obtained by the principal component linear encoding and then processes it with energy regularization to improve the classification efficiency further. The experimental results demonstrate that this method increases the recognition rate average over 1% than existing algorithms.

    Reference
    [1] Joachims T. Text categorization with support vector machines: Learning with many relevant features. In: Nédellec C, Rouveirol C, eds. Proc. of the Machine Learning: ECML'98. Heidelberg: Springer-Verlag, 1998. 137-142.
    [2] Tong S, Koller D. Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research, 2002,2:45-66.
    [3] Yang JC, Yu K, Gong YH, Huang T. Linear spatial pyramid matching using sparse coding for image classification. In: Proc. of the Computer Vision and Pattern Recognition. Miami, 2009. 1794-1801.
    [4] Wang JJ, Yang JC, Yu K, LÜ FJ, Huang T, Gong YH. Locality-Constrained linear coding for image classification. In: Proc. of the Computer Vision and Pattern Recognition. San Francisco, 2010. 3360-3367.
    [5] Csurka G, Bray C, Dance C, Fan L. Visual categorization with bags of keypoints. In: Proc. of the Workshop on statistical learning in computer vision, ECCV. Prague, 2004. 1-22.
    [6] Van Gemert JC, Geusebroek JM, Veenman C, Smeulders AWM. Kernel codeboks for scene categorization. In: Forsyth D, Torr P, Zisserman A, eds. Proc. of the Computer Vision-ECCV 2008. Berlin, Heidelberg: Springer-Verlag, 2008. 696-709.
    [7] J. Sivic, B. C. Russell, A. A. Elfros, A. Zisserman, and W. T. Freeman. Discovering objects and their location in images. In: Proc. of the Computer Vision and Pattern Recognition. 2005. 370-377.
    [8] Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 1970,12(1):55-67.
    [9] Massy WF. Principal components regression in exploratory statistical research. Journal of the American Statistical Association, 1965,60(309):234-256.
    [10] Wang YJ. Expressive methods of visual object based on remarkable local feature [Ph.D. Thesis]. Beijing: Beijing University of Technology, 2010 (in Chinese with English abstract).
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

艾浩军,张敏,方禹,赵梦蕾,李泰舟,王红霞.视觉词汇的主成分线性编码方法.软件学报,2013,24(S2):42-49

Copy
Share
Article Metrics
  • Abstract:2662
  • PDF: 4958
  • HTML: 0
  • Cited by: 0
History
  • Received:June 15,2012
  • Revised:July 22,2013
  • Online: January 02,2014
You are the first2038304Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063