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
来源
COLOR IMAGING XIX: DISPLAYING, PROCESSING, HARDCOPY, AND APPLICATIONS | 2014年 / 9015卷
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
  • [31] Application of SVM-Based Filter Using LMS Learning Algorithm for Image Denoising
    Lin, Tzu-Chao
    Yeh, Chien-Ting
    Liu, Mu-Kun
    NEURAL INFORMATION PROCESSING: MODELS AND APPLICATIONS, PT II, 2010, 6444 : 82 - 90
  • [32] SVM based Chinese web page automatic classification
    Liang, JZ
    2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 2265 - 2268
  • [33] A SVM-based Multi-dimension Factor Decision-making Model Framework
    Nie, Peng
    2016 INTERNATIONAL CONFERENCE ON APPLIED MECHANICS, ELECTRONICS AND MECHATRONICS ENGINEERING (AMEME 2016), 2016, : 89 - 94
  • [34] Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels
    Utkin, Lev V.
    Chekh, Anatoly I.
    Zhuk, Yulia A.
    NEURAL NETWORKS, 2016, 80 : 53 - 66
  • [35] A real-time SVM-based hardware accelerator for hyperspectral images classification in FPGA
    Martins, Lucas Amilton
    Viel, Felipe
    Seman, Laio Oriel
    Bezerra, Eduardo Augusto
    Zeferino, Cesar Albenes
    MICROPROCESSORS AND MICROSYSTEMS, 2024, 104
  • [36] Multiple Kernel Learning SVM-Based EMG Pattern Classification for Lower Limb Control
    She, Qingshan
    Luo, Zhizeng
    Meng, Ming
    Xu, Ping
    11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2010), 2010, : 2109 - 2113
  • [37] Identification of SVM-based classification model, synthesis and evaluation of prenylated flavonoids as vasorelaxant agents
    Dong, Xiaowu
    Liu, Yujie
    Yan, Jingying
    Jiang, Chaoyi
    Chen, Jing
    Liu, Tao
    Hu, Yongzhou
    BIOORGANIC & MEDICINAL CHEMISTRY, 2008, 16 (17) : 8151 - 8160
  • [38] SVM-based multi-state-mapping approach for multi-class classification
    Liu, Bo
    Xiao, Yanshan
    Cao, Longbing
    KNOWLEDGE-BASED SYSTEMS, 2017, 129 : 79 - 96
  • [39] Classification of Remote Sensing Image Based on Combined SVM
    Yin, F.
    Hu, G. S.
    Wang, J.
    INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENVIRONMENTAL ENGINEERING (CSEE 2015), 2015, : 1242 - 1248
  • [40] SVM-based decision for power transformers fault diagnosis using Rogers and Doemenburg ratios DGA
    Souahlia, Seifeddine
    Bacha, Khmais
    Chaari, Abdelkader
    2013 10TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD), 2013,