Image retrieval based on multi-feature integration and Adaboost relevance feedback

Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (36): 137-140.

• 网络、通信与安全 • Previous Articles Next Articles

Image retrieval based on multi-feature integration and Adaboost relevance feedback

ZHU Shou-ye

School of Information and Media，Xuzhou Normal University，Xuzhou，Jiangsu 221009，China

Received:1900-01-01 Revised:1900-01-01 Online:2007-12-21 Published:2007-12-21
Contact: ZHU Shou-ye

基于多特征融合和Adaboost算法的图像检索

朱守业

徐州师范大学信息传播学院，江苏徐州 221009

通讯作者: 朱守业

Abstract

Abstract: A image retrieval method based on multi-feature integration is proposed.Images are firstly partitioned into equal-sized sub-images，then the main color of each block is extracted and the main color histogram is computed as the image’s color feature.Gabor wavelet is proposed to describe the texture feature，and wavelet invariant moments is used to describe shape feature．In order to improve the precision of image retrieval，a relevance feedback mechanism，based on Adaboost method is invoked.Adaboost method is used to realize the dimensionality reduction and improve retrieval speed.Finally，the precisions and recalls of single feature，multi-feature integration and relevance feedback are calculated and compared．

Key words: image retrieval, feature integration, relevance feedback, Adaboost method

摘要： 提出了一种多特征融合的图像检索方法。首先将图像进行分块，并提取分块主色，然后采用主色直方图作为图像的颜色特征。同时，提出采用Gabor小波描述图像的纹理特征，采用小波矩描述形状特征，最后将三种不同特征进行融合的检索方法。为了提高图像检索的准确度，提出Adaboost的相关反馈算法，在反馈过程中，Adaboost算法对特征进行降维，加快检索的速度。最后分别给出基于单一特征，特征融合和相关反馈方法的查准率和查全率，并对实验结果进行分析。

关键词: 图像检索, 特征融合, 相关反馈, Adaboost算法

ZHU Shou-ye. Image retrieval based on multi-feature integration and Adaboost relevance feedback[J]. Computer Engineering and Applications, 2007, 43(36): 137-140.

朱守业. 基于多特征融合和Adaboost算法的图像检索[J]. 计算机工程与应用, 2007, 43(36): 137-140.

[1]	WU Guanghua, ZHANG Xudong, GE Wei, SUN Ge, MAO Caisheng. Image Copy Detection Based on Model Ensemble and Feature Fusion [J]. Computer Engineering and Applications, 2020, 56(20): 199-205.
[2]	CHEN Xiang, HE Heng, LI Peng, JIN Yu, NIE Lei. Ciphertext Image Retrieval Scheme Based on Target Detection in Cloud Environment [J]. Computer Engineering and Applications, 2020, 56(11): 75-82.
[3]	WANG Miao, JING Junfeng. Image Retrieval Based on Hash Coding and Convolutional Neural Network [J]. Computer Engineering and Applications, 2019, 55(23): 194-199.
[4]	NIU Yaxi, JI Xiaoping. Image Retrieval Algorithm Based on Convolutional Neural Network [J]. Computer Engineering and Applications, 2019, 55(18): 201-206.
[5]	CAI Pengfei, YE Jianfeng. CBIR Method Based on Improved CNN and Bilinear Model [J]. Computer Engineering and Applications, 2019, 55(16): 191-196.
[6]	DUAN Wenjing, CHEN Shaoping. Discrete Hash Image Retrieval Algorithm with High-Level Semantic Features [J]. Computer Engineering and Applications, 2019, 55(13): 212-217.
[7]	ZHENG Dongting, DONG Yun, ZHU Xingchi, ZHOU Yuan. New Spatial Relational Feature for Image Retrieval [J]. Computer Engineering and Applications, 2019, 55(10): 1-8.
[8]	MU Yakun, FENG Shengwei, ZHANG Jing. Image Retrieval Based on Text and Semantic Relevance Analysis [J]. Computer Engineering and Applications, 2019, 55(1): 196-202.
[9]	WANG Weijing, ZHANG Xuefeng. Cancelable Palmprint Template based on Gabor and Local Directional Pattern [J]. Computer Engineering and Applications, 2018, 54(9): 89-95.
[10]	MIAO Jun1, CUI Song2, DUAN Lijuan2, ZHANG Xuan2, XU Shaowu1. Image retrieval with longest common visual substring [J]. Computer Engineering and Applications, 2018, 54(15): 192-196.
[11]	WANG Qihao1，2, WANG Bin1，2. Local matching algorithm for product image search [J]. Computer Engineering and Applications, 2017, 53(6): 246-251.
[12]	XIONG Guoping. Image retrieval algorithm by GLCM through transforming SUSAN edge response matrix to grayscale matrix [J]. Computer Engineering and Applications, 2016, 52(5): 225-230.
[13]	XIE Li1, CHENG Yun1, ZENG Jiexian2, YU Sheng1. Image retrieval based on color and motif gradient direction co-occurrence histogram [J]. Computer Engineering and Applications, 2016, 52(10): 181-186.
[14]	CHEN Xi, XIAO Jian. Edge histogram for sketch based image retrieval using nonsubsampled contourlet edge detection.Computer Engineering and Applications [J]. Computer Engineering and Applications, 2015, 51(22): 172-175.
[15]	WU Jialiang, LIANG Zhengyou, LIN Shuo. Efficient clustering index table image retrieval [J]. Computer Engineering and Applications, 2013, 49(9): 183-186.

Image retrieval based on multi-feature integration and Adaboost relevance feedback

基于多特征融合和Adaboost算法的图像检索

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics