作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2011, Vol. 37 ›› Issue (3): 204-206. doi: 10.3969/j.issn.1000-3428.2011.03.072

• 图形图像处理 • 上一篇    下一篇

基于NACT和HMT模型的纹理图像检索

尚赵伟1,2,赵正辉1,庞庆堃1,翟振兴1,李 剑1,杨建伟3   

  1. (1. 重庆大学计算机学院,重庆 400030;2. 四川省模式识别与智能信息处理重点实验室,成都 610106; 3. 南京信息工程大学数理学院,南京 210044)
  • 出版日期:2011-02-05 发布日期:2011-01-28
  • 作者简介:尚赵伟(1968-),男,副教授,主研方向:数字图像处理,模式识别;赵正辉,硕士研究生;庞庆堃、翟振兴、李 剑,硕士;杨建伟,教授
  • 基金资助:
    国家自然科学基金资助重点项目(90820306);国家自然科学基金资助面上项目(60973157);中央高校基本科研业务费专项基金资助项目(CDJRC10180009)

Texture Image Retrieval Based on Non-Aliasing Contourlet Transform and Hidden Markov Tree Model

SHANG Zhao-wei 1,2, ZHAO Zheng-hui 1, PANG Qing-kun 1, ZHAI Zhen-xing 1 , LI Jian 1, YANG Jian-wei 3   

  1. (1. College of Computer Science, Chongqing University, Chongqing 400030, China; 2. Sichuan Provincial Key Laboratory of Pattern Recognition and Intelligent Information Processing, Chengdu 610106, China; 3. College of Mathematics and Physics, Nanjing University of Information Engineering, Nanjing 210044, China)
  • Online:2011-02-05 Published:2011-01-28

摘要: 针对轮廓波变换方向子带中的频谱混叠现象及传统KLD方法度量隐马尔科夫模型间距离的局限性,提出结合改进KLD度量的抗混叠轮廓波隐马尔科夫树(HMT)纹理图像检索方法。利用抗混叠轮廓波变换抑制频谱混叠的特点对纹理进行分解,建立HMT模型并将其训练后的参数集视为纹理特征,利用改进KLD方法满足三角不等式的优点度量HMT模型间的距离,提高纹理图像检索精度。理论和实验结果表明,该算法的查准率比CT-HMT+传统KLD方法提高了2.81%。

关键词: 轮廓波, 抗混叠轮廓波变换, 隐马尔科夫树, 改进KLD方法

Abstract: Aiming at spectrum aliasing problems in the directional subbands during the contourlet transform, considering the limitation of the traditional measure KL Distance(KLD) between two hidden Markov models, this paper proposes a texture image retrieval method based on improved KLD, using Non-Aliasing Contourlet Transform(NACT) Hidden Markov Tree(HMT) model. The algorithm uses NACT to decompose a texture, which can deal with the spectrum aliasing phenomenon well, trains the HMT model and takes the HMT model parameter set as the texture features. It computes the similarity between two models using improved KLD, which meets the triangle inequality properties and can measure the distance better. The proposed algorithm is verified by theory and experiment, and results show that the precision of proposed method improves 2.81 percent than that of the CT-HMT combining traditional KLD measure method.

Key words: contourlet, Non-Aliasing Contourlet Transform(NACT), Hidden Markov Tree(HMT), improved KLD method

中图分类号: