SVM-based automatic scanned image classification with quick decision capability

被引:2
|
作者
Lu, Cheng [1 ]
Wagner, Jerry [2 ]
Pitta, Brandi [2 ]
Larson, David [2 ]
Allebach, Jan [1 ,2 ]
机构
[1] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
[2] Hewlett Packard Corp, Boise, ID 83706 USA
关键词
Digital copier; classification; support vector machine;
D O I
10.1117/12.2047335
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Digital copiers are now widely used. One major issue for a digital copier is copy quality. In order to achieve as high quality as possible for every input document, multiple processing pipelines are included in a digital copier. Every processing pipeline is designed specifically for a certain class of document, which may be text, picture, or a mixture of both as is illustrated by the three examples shown in Fig. 1. In this paper, we describe an algorithm that can effectively classify an input image into its corresponding category.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] SVM-based automatic classification for protein structural domain
    Shao, Xiao-Han
    Tian, Ying-Jie
    Deng, Nai-Yang
    OPTIMIZATION AND SYSTEMS BIOLOGY, 2007, 7 : 341 - +
  • [2] An Accurate SVM-Based Classification Approach for Hyperspectral Image Classification
    Baassou, Belkacem
    He, Mingyi
    Mei, Shaohui
    2013 21ST INTERNATIONAL CONFERENCE ON GEOINFORMATICS (GEOINFORMATICS), 2013,
  • [3] Research on SVM-Based Automatic Classification of Chinese Web Page
    Song, Jie
    Liu, Yanque
    Li, Nana
    Gu, Junhua
    PROGRESS IN INTELLIGENCE COMPUTATION AND APPLICATIONS, 2008, : 160 - 164
  • [4] Hyperspectral Image Classification with SVM-based Domain Adaption Classifiers
    Sun, Zhuo
    Wang, Cheng
    Li, Peng
    Wang, Hanyun
    Li, Jonathan
    PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMPUTER VISION IN REMOTE SENSING, 2012, : 268 - 272
  • [5] SVM-based hyperspectral image classification using intrinsic dimension
    Hasanlou M.
    Samadzadegan F.
    Homayouni S.
    Arabian Journal of Geosciences, 2015, 8 (1) : 477 - 487
  • [6] Customizing kernel functions for SVM-based hyperspectral image classification
    Guo, Baofeng
    Gunn, Steve R.
    Damper, R. I.
    Nelson, James D. B.
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2008, 17 (04) : 622 - 629
  • [7] SVM-based hyperspectral image classification using intrinsic dimension
    Hasanlou, Mahdi
    Samadzadegan, Farhad
    Homayouni, Saeid
    ARABIAN JOURNAL OF GEOSCIENCES, 2015, 8 (01) : 477 - 487
  • [8] A SVM-based classification selection algorithm for the automatic selection of guide star
    Zheng, S
    Xiong, CY
    Wu, WR
    Tian, JW
    Liu, J
    THIRD INTERNATIONAL SYMPOSIUM ON MULTISPECTRAL IMAGE PROCESSING AND PATTERN RECOGNITION, PTS 1 AND 2, 2003, 5286 : 175 - 178
  • [9] Detecting Neural Decision Patterns Using SVM-based EEG Classification
    Paul, Padma Polash
    Leung, Howard
    Peterson, D. A.
    Sejnowski, T. J.
    Poizner, Howard
    2010 4TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING (ICBBE 2010), 2010,
  • [10] An SVM-based distal lung image classification using texture descriptors
    Desir, Chesner
    Petitjean, Caroline
    Heutte, Laurent
    Thiberville, Luc
    Salauen, Mathieu
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2012, 36 (04) : 264 - 270