计算机科学 ›› 2010, Vol. 37 ›› Issue (11): 282-286.

• 图形图像 • 上一篇    下一篇

基于多特征融合的花卉图像检索

柯逍,陈小芬,李绍滋   

  1. (厦门大学智能科学与技术系 厦门361005);(福建省仿脑智能系统重点实验室 厦门361005);(厦门大学计算机科学系 厦门361005)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(60873179,60803078),高等学校博士学科点专项科研基金(20090721710032),深圳市科技计划基础研究项目(JC200903180630A)资助。

Flower Image Retrieval Based on Multi-features Fusion

KE Xiao,CHEN Xiao-fen,LI Shao-zi   

  • Online:2018-12-01 Published:2018-12-01

摘要: 以植物花卉图像为研究对象,对植物花卉图像在区域分割、特征提取、基于内容的雷同图像过滤以及基于SVM的植物花卉图像检索等方面进行了系统、深入和较为全面的研究。首先为保证检索效果,提出了基于Canny边缘的雷同图片过滤算法对花卉图像库中的雷同图片进行过滤。然后提出基于2RGB混合颜色模型的自适应阂值分割算法来对花卉图像进行分割。对特征提取采用多特征融合的方法,其中针对形状特征提出了基于HSV颜色模型的加权不变矩,并提出了结合形状特征与纹理特征的边缘LBP算子。通过在花卉图像库进行的实验表明,提出的若干方法都是有效的。

关键词: 花卉检索,基于内容的图像检索,区域分割,特征提取,雷同图片过滤

Abstract: This paper had systematic and overall researches on flower images, including regional segmentation, feature extraction, content based duplicate images filtering and SVM-based image retrieval, etc. Firstly, in order to ensure retrieval results, we proposed duplicate images filtering algorithm based on Canny edge to detect duplicate images. Then we proposed adaptive threshold segmentation algorithm based on 2RGB mixed color model to segment flower images.Using multi-feature fusion strategy for feature extraction, we proposed weighted invariant moment shape based on HSV color model, as well as edge LBP feature which both combined texture feature and shape feature. Finally, we executed experiments on flower image library, comparative results show that above algorithms are effective.

Key words: Flowers retrieval, Content based image retrieval, Regional segmentation, Feature extraction, Duplicate image filtering

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