Mining frequent spatial patterns in image databases

被引：0
|
作者：
Chen, Wei-Ta [1 ]
Chen, Yi-Ling [1 ]
Chen, Ming-Syan [1 ]
机构：
[1] Natl Taiwan Univ, Dept Elect Engn, Taipei 10764, Taiwan
来源：
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2006年 / 3918卷
关键词：
image mining; spatial pattern;
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：
Mining useful patterns in image databases can not only reveal useful information to users but also help the task of data management. In this paper, we propose an image mining framework, Frequent Spatial Pattern mining in images (FSP), to mine frequent patterns located in a pair of spatial locations of images. A pattern in the FSP is associated with a pair of spatial locations and refers to the occurrence of the same image content in a set of images. This framework is designed to be general so as to accept different levels of representations of image content and different layout forms of spatial representations.
引用
收藏
页码：699 / 703
页数：5
相关论文
共 50 条
  • [31] Mining statistical information of frequent fault-tolerant patterns in transactional databases
    Poernorno, Ardian Kristanto
    Gopalkrishnan, Vivekanand
    [J]. ICDM 2007: PROCEEDINGS OF THE SEVENTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, 2007, : 272 - 281
  • [32] Mining frequent patterns with multiple item support thresholds in tourism information databases
    Chen, Yi-Chun
    Lin, Grace
    Chan, Ya-Hui
    Shih, Meng-Jung
    [J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8916 : 89 - 98
  • [33] Frequent Itemset Mining in Multirelational Databases
    Jimenez, Aida
    Berzal, Fernando
    Cubero, Juan-Carlos
    [J]. FOUNDATIONS OF INTELLIGENT SYSTEMS, PROCEEDINGS, 2009, 5722 : 15 - 24
  • [34] Mining frequent iternsets in evolving databases
    Veloso, AA
    Meira, W
    de Carvalho, MB
    Pôssas, B
    Parthasarathy, S
    Zaki, MJ
    [J]. PROCEEDINGS OF THE SECOND SIAM INTERNATIONAL CONFERENCE ON DATA MINING, 2002, : 494 - 510
  • [35] Mining Co-location Patterns in Incremental Spatial Databases
    Chang, Ye-In
    Wu, Chen-Chang
    Yen, Ching-Yi
    [J]. 2022 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (IEEE BIGCOMP 2022), 2022, : 141 - 148
  • [36] An Improved Design Approach in Spatial Databases Using Frequent Association Rule Mining Algorithm
    Tripathy, Animesh
    Das, Subhalaxmi
    Patra, Prashanta Kumar
    [J]. 2010 IEEE 2ND INTERNATIONAL ADVANCE COMPUTING CONFERENCE, 2010, : 410 - +
  • [37] Fast and Efficient Mining of Frequent and Maximal Periodic Patterns in Spatiotemporal Databases for shifted instances
    Obulesu, O.
    Reddy, A. Rama Mohan
    [J]. 2016 IEEE 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (IACC), 2016, : 35 - 40
  • [38] H-mine: Hyper-structure mining of frequent patterns in large databases
    Pei, J
    Han, JW
    Lu, HJ
    Nishio, S
    Tang, SW
    Yang, DQ
    [J]. 2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2001, : 441 - 448
  • [39] Mining Top-k Minimal Redundancy Frequent Patterns over Uncertain Databases
    Wang, Haishuai
    Zhang, Peng
    Wu, Jia
    Pan, Shirui
    [J]. NEURAL INFORMATION PROCESSING, ICONIP 2015, PT IV, 2015, 9492 : 111 - 119
  • [40] Constructing complete FP-tree for incremental mining of frequent patterns in dynamic databases
    Adnan, Muhaimenul
    Alhajj, Reda
    Barker, Ken
    [J]. ADVANCES IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 4031 : 363 - 372
12345