The Image Retrieval Based on Attribute Feature Extraction Coupled Firefly Optimization

ZHAO Kai; HAN Yi; LUO Sheng-yong

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The Image Retrieval Based on Attribute Feature Extraction Coupled Firefly Optimization

更新时间：2026-09-04

- The Image Retrieval Based on Attribute Feature Extraction Coupled Firefly Optimization
- Vol. 34, Issue 11, Pages: 31-36(2017)
- 作者机构：
  
  1. 安阳工学院机构计算机科学与信息工程,河南,安阳,455000
  2. 华中科技大学国家NC工程技术研究中心,湖北,武汉,430000
  3. 北京科技大学计算机与通信工程学院,北京,100083
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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赵凯, 韩毅, 罗圣勇. 基于属性特征提取与萤火虫优化的图像检索算法[J]. 微电子学与计算机, 2017, 34(11): 31-36.

ZHAO Kai, HAN Yi, LUO Sheng-yong. The Image Retrieval Based on Attribute Feature Extraction Coupled Firefly Optimization[J]. 2017, 34(11): 31-36.
赵凯, 韩毅, 罗圣勇. 基于属性特征提取与萤火虫优化的图像检索算法[J]. 微电子学与计算机, 2017, 34(11): 31-36. DOI：

ZHAO Kai, HAN Yi, LUO Sheng-yong. The Image Retrieval Based on Attribute Feature Extraction Coupled Firefly Optimization[J]. 2017, 34(11): 31-36. DOI：

摘要

为了解决图像检索算法中存在的语义鸿沟问题

并提高图像检索性能

本文提出了基于属性特征提取耦合萤火虫优化的图像检索技术.首先

基于像素聚类特性

利用归一化分割模型将每个图像分割成不同的区域

并提取每个区域的颜色和纹理特征; 然后

引入Earth Movers distance(EMD)相似度量

计算搜索目标与图像库中目标的距离

从而获得查询目标的反馈结果; 再通过支持向量机(Support Vector Machine

SVM)对获得的图像反馈结果进行学习

并引入萤火虫算法进行迭代优化

对相关图像和不相关图像进行持续筛选

从而获得用户期望结果.通过在Corel图像库中实验证明: 与当前常用的图像检索算法相比

本文算法具有更高查准率和查全率

以及更低的平均归一化检索率与复杂度

能准确快速地查找搜索目标.

Abstract

In order to solve the problem of semantic gap in image retrieval algorithm

and improve the performance of image retrieval

proposed a Image retrieval based on the optimization of regional segmentation coupled firefly algorithm.Firstly

based on the characteristics of pixel clustering

each image was segmented into different regions and the color and texture features were extracted by using the normalized segmentation model; Then

the Movers distance Earth (EMD) criterion was introduced to calculate the distance between the query image and the database image

get the feedback from the nearest image and the users request; Then through the SVM to get the image feedback information for classification and learning

and the introduction of firefly algorithm for iterative optimization

Through continuous iteration

relevant and not related to the continuous image filtering

so as to obtain the desired results.Through in the Corel image database experiments show that: compared with the commonly used image retrieval algorithm

this algorithm has higher precision and recall

ANMRR lower value

the efficiency of the algorithm has been improved

can be more accurate to find users expect image.

关键词

Keywords

references

张鑫, 温显斌, 孟庆霞. 基于颜色特征的图像检索方法研究[J]. 计算机科学, 2012, 39(11): 243-246. DOI: 243

欧锁阳, 冯琦, 张堑. 一种融合对数极坐标映射和归一化割的图像分割算法[J]. 计算机应用研究, 2012, 29(10): 3990-3992. DOI: 3990

Manjunath B S, Ohm J R, Vasduvanvv, et al. Color and texture descriptors[J]. IEEE Trans on Circuits and Systems for Video Technology, 2011, 11(6): 703-715. DOI: 703

Muchenxuan Tong, Kun-Hong Liu, Chungui Xu. An ensemble of SVM classifiers based on gene pairs [J]. Computers in Biology and Medicine, 2013, 43(6): 729-737. DOI: 729

Avi Arampatzis, Konstantinos Zagoris. Dynamic two-stage image retrieval from large multimedia databases [J]. Information Processing and Management, 2013, 49(6): 274-285. DOI: 274

刘鹏, 刘弘, 郑向伟. 基于改进萤火虫算法的动态自动聚集路径规划方法[J]. 计算机应用研究, 2011, 28(11): 4146-4149. DOI: 4146

Hariprasad Reddy A, Subhash Chandra N. Local oppugnant color space extrema patterns for content based natural and texture image retrieval [J]. International Journal of Electronics and Communications, 2014, 69(12): 290-298. DOI: 290

王吉权, 王福林. 萤火虫算法的改进分析及应用[J]. 计算机应用, 2014, 34(9): 2552-2556. DOI: 2552

Minhee Jang, Sangwook Kim, Christos Faloutsos. Accurate approximation of the earth movers distance in linear time [J]. Journal of Computer Science%Technology, 2014, 29(1): 142-154. DOI: 142

符祥, 曾接贤. 基于兴趣点匹配和空间分布的图像检索方法[J]. 中国激光, 2013, 37(3): 74-778. DOI: 74

Kanimozhi N T, Latha K. An integrated approach to region based image retrieval using firefly algorithm and support vector machine[J]. Neurocomputing, 2015, 151(13): 1099-1111. DOI: 1099

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