LOGO DETECTION BASED ON SPATIAL-SPECTRAL SALIENCY AND PARTIAL SPATIAL CONTEXT

被引：0
|
作者：
Gao, Ke [1 ]
Lin, Shouxun [1 ]
Zhang, Yongdong [1 ]
Tang, Sheng [1 ]
Zhang, Dongming [1 ]
机构：
[1] Chinese Acad Sci, Inst Comp Technol, Lab Adv Comp Res, Beijing 100080, Peoples R China
来源：
ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3 | 2009年
关键词：
Logo detection; spatial-spectral saliency; partial spatial context;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Logo detection is important for brand advertising and surveillance applications. The central issues of this technology are fast localization and accurate matching. Based on key traits analysis of common logos, this paper presents a two-stage detection scheme based on spatial-spectral saliency (SSS) and partial spatial context (PSC). SSS speeds up logo location and avoid the impact of cluttered background. PSC filters false matching using spatial consistency of local invariant points. The integration of SSS and PSC result in faster localization and increased accuracy. Experiments on a dataset of nearly 10,000 web images containing several popular logo types are presented. The results indicate that our method is applicable and precise for different logo detection scenarios.
引用
收藏
页码：322 / 329
页数：8
相关论文
共 50 条
  • [1] RGB-D Visual Saliency Detection Method Based on Spatial-Spectral Mixture Analysis
    [J]. Yuan, Xia (yuanxia@njust.edu.cn), 1600, Chinese Academy of Sciences (39):
  • [2] Hyperspectral anomaly detection based on spatial-spectral multichannel autoencoders
    Jia, Sen
    Liu, Kuan
    Xu, Meng
    Zhu, Jiasong
    [J]. National Remote Sensing Bulletin, 2024, 28 (01) : 55 - 68
  • [3] Spatial-Spectral Extraction for Hyperspectral Anomaly Detection
    Hu, Jing
    Zhang, Yujing
    Zhao, Minghua
    Li, Peng
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [4] Saliency detection by combining spatial and spectral information
    Zhang, Yanbang
    Han, Junwei
    Guo, Lei
    [J]. OPTICS LETTERS, 2013, 38 (11) : 1987 - 1989
  • [5] Evaluation of a change detection method based on joint spatial-spectral information
    Izquierdo, EM
    Martín, CG
    Hidalgo, AA
    Saavedra, ML
    [J]. REMOTE SENSING IN TRANSITION, 2004, : 121 - 126
  • [6] Spatial-spectral preprocessing for spectral unmixing
    Yan, Yang
    Hua, Wenshen
    Liu, Xun
    Cui, Zihao
    Diao, Dongmei
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2019, 40 (04) : 1357 - 1373
  • [7] Spatial-Spectral Terahertz Networks
    Lin, Zheng
    Wang, Lifeng
    Tan, Bo
    Li, Xiang
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2022, 21 (06) : 3881 - 3892
  • [8] Acoustic-seismic mine detection based on spatial-spectral distribution of poles
    Yu, SH
    Witten, TR
    Mehra, RK
    [J]. DETECTION AND REMEDIATION TECHNOLOGIES FOR MINES AND MINELIKE TARGETS VIII, PTS 1 AND 2, 2003, 5089 : 606 - 613
  • [9] Forest Disaster Detection Method Based on Ensemble Spatial-Spectral Genetic Algorithm
    Cao, Yang
    Feng, Wei
    Quan, Yinghui
    Bao, Wenxing
    Dauphin, Gabriel
    Ren, Aifeng
    Yuan, Xiaoguang
    Xing, Mengdao
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 : 7375 - 7390
  • [10] Forest Disaster Detection Method Based on Ensemble Spatial-Spectral Genetic Algorithm
    Cao, Yang
    Feng, Wei
    Quan, Yinghui
    Bao, Wenxing
    Dauphin, Gabriel
    Ren, Aifeng
    Yuan, Xiaoguang
    Xing, Mengdao
    [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15 : 7375 - 7390
12345