A clustering-based possibilistic method for image classification

被引：0
作者：
Drummond, I [1 ]
Sandri, S [1 ]
机构：
[1] Inst Nacl Pesquisas Espaciais, BR-12227010 Sao Jose Dos Campos, SP, Brazil
来源：
ADVANCES IN ARTIFICIAL INTELLIGENCE - SBIA 2004 | 2004年 / 3171卷
关键词：
image classification; clustering; possibility theory; similarity relation;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work proposes a general image classification method, based in possibility theory and clustering. We illustrate our approach with a CBERS image and compare the results obtained by applying our method to other classification methods.
引用
收藏
页码：454 / 463
页数：10
相关论文
共 12 条
  • [1] [Anonymous], 1987, FUZZY SETS APPL SELE
  • [2] FCM - THE FUZZY C-MEANS CLUSTERING-ALGORITHM
    BEZDEK, JC
    EHRLICH, R
    FULL, W
    [J]. COMPUTERS & GEOSCIENCES, 1984, 10 (2-3) : 191 - 203
  • [3] Bishop M.M., 1975, DISCRETE MULTIVARIAT
  • [4] A user-friendly system for Synthetic Aperture Radar image classification based on grayscale distributional properties and context
    Frery, AC
    Yanasse, CDF
    Vieira, PR
    SantAnna, SJS
    Renno, CD
    [J]. X BRAZILIAN SYMPOSIUM ON COMPUTER GRAPHICS AND IMAGE PROCESSING, PROCEEDINGS, 1997, : 211 - 218
  • [5] *INPE, 2004, CBERS PROGR
  • [6] MALINSKI P, 2001, P FUZZ IEEE 01 MELB
  • [7] MORAES RM, 2000, INTELLIGENT TECHNIQU, P271
  • [8] ROUX L, 1997, FUZZY INFORMATION EN, P111
  • [9] RUBERT C, UNPUB INTEGRATED NEU
  • [10] Setnes M., 2000, ERIM REPORT SERIES R
12