Multi-Feature Fusion Image Retrieval Algorithm Based on Fuzzy Color

被引:0
作者
Gao, Yue [1 ,2 ]
Wan, Wanggen [1 ,2 ]
机构
[1] Shanghai Univ, Sch Commun & Informat Engineer, Shanghai, Peoples R China
[2] Shanghai Univ, Inst Smartc, Shanghai, Peoples R China
来源
2018 INTERNATIONAL CONFERENCE ON AUDIO, LANGUAGE AND IMAGE PROCESSING (ICALIP) | 2018年
关键词
image retrieval; rectangular block; fuzzy quantization; edge directional descriptors; Hu moments;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
How to improve the accuracy of content-based image retrieval algorithms, the key lies in feature extraction. Because the performance of single feature retrieval is poor, feature fusion is performed using multiple features of the image. First, the image is partitioned by rectangle, then the color feature and texture feature are extracted by fuzzy quantization HSV color space and edge directional descriptors. In addition, the Hu moments are used to extract the shape features of the image, and finally the three kinds of image underlying features are weighted and combined in series. Search. Experiments show that the multi feature fusion algorithm based on fuzzy color can better describe the image features and improve the retrieval efficiency.
引用
收藏
页码:262 / 265
页数:4
相关论文
共 50 条
  • [1] Multi-feature image retrieval algorithm based on block color weighting
    Zhang Ye
    2018 INTERNATIONAL CONFERENCE ON SENSOR NETWORKS AND SIGNAL PROCESSING (SNSP 2018), 2018, : 217 - 221
  • [2] Image Retrieval Based on Multi-Feature Similarity Score Fusion Using Genetic Algorithm
    Chen, Mianshu
    Fu, Ping
    Sun, Yuan
    Zhang, Hui
    2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2010), VOL 2, 2010, : 46 - 49
  • [3] Beauty Product Image Retrieval Based on Multi-Feature Fusion and Feature Aggregation
    Wang, Qi
    Lai, Jingxiang
    Xu, Kai
    Liu, Wenyin
    Lei, Liang
    PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18), 2018, : 2063 - 2067
  • [4] An Adaptive Weight Method for Image Retrieval Based Multi-Feature Fusion
    Lu, Xiaojun
    Wang, Jiaojuan
    Li, Xiang
    Yang, Mei
    Zhang, Xiangde
    ENTROPY, 2018, 20 (08)
  • [5] Content-based image retrieval technology using multi-feature fusion
    Huang, Min
    Shu, Huazhong
    Ma, Yaqiong
    Gong, Qiuping
    OPTIK, 2015, 126 (19): : 2144 - 2148
  • [6] Digital Image Retrieval Technology Based on Multi-feature
    Wang, Zhujun
    Zhang, Guangwen
    2013 IEEE 4TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC), 2014, : 16 - 19
  • [7] The Image Retrieval Algorithm Based on Color Feature
    Chen, YuanYong
    PROCEEDINGS OF 2016 IEEE 7TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2016), 2016, : 647 - 650
  • [8] A Novel Multi-Feature Fusion and Sparse Coding-Based Framework for Image Retrieval
    Chen, Qiaosong
    Ding, Yuanyuan
    Li, Hai
    Wang, Xi
    Wang, Jin
    Deng, Xin
    2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), 2014, : 2391 - 2396
  • [9] Multi-feature fusion for image retrieval using constrained dominant sets
    Alemu, Leulseged Tesfaye
    Pelillo, Marcello
    IMAGE AND VISION COMPUTING, 2020, 94
  • [10] Multi-Feature Fusion with SVM Classification for Crime Scene Investigation Image Retrieval
    Liu, Ying
    Wang, Fuping
    Hu, Dan
    Fan, Jiulun
    2017 IEEE 2ND INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP), 2017, : 160 - 165