Image retrieval based on color features integrated with anisotropic directionality

被引:8
|
作者
Bai, Jing [1 ]
Wang, Xiaohua [1 ]
Jiao, Licheng [1 ]
机构
[1] Xidian Univ, Inst Intelligent Informat Proc, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China
基金
中国国家自然科学基金; 国家高技术研究发展计划(863计划);
关键词
image retrieval; color histogram; directionlet transform; anisotropic; similarity;
D O I
10.3969/j.issn.1004-4132.2010.01.020
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A novel image retrieval approach based on color features and anisotropic directional information is proposed for content based image retrieval systems (CBIR). The color feature is described by the color histogram (CH), which is translation and rotation invariant. However, the CH does not contain spatial information which is very important for the image retrieval. To overcome this shortcoming, the subband energy of the lifting directionlet transform (L-DT) is proposed to describe the directional information, in which L-DT is characterized by multi-direction and anisotropic basis functions compared with the wavelet transform. A global similarity measure is designed to implement the fusion of both color feature and anisotropic directionality for the retrieval process. The retrieval experiments using a set of COREL images demonstrate that the higher query precision and better visual effect can be achieved.
引用
收藏
页码:127 / 133
页数:7
相关论文
共 50 条
  • [31] Color histogram features based image classification in content-based image retrieval systems
    Sergyan, Szabolcs
    2008 6TH INTERNATIONAL SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS, 2008, : 206 - 209
  • [32] Color and Texture Features for Image Indexing and Retrieval
    Murala, Subrahmanyam
    Balaji, Anil
    Maheshwari, Gonde R. P.
    2009 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE, VOLS 1-3, 2009, : 1411 - 1416
  • [33] Combining Color and Shape Features for Image Retrieval
    Lee, XiaoFu
    Yin, Qian
    UNIVERSAL ACCESS IN HUMAN-COMPUTER INTERACTION: APPLICATIONS AND SERVICES, PT III, 2009, 5616 : 569 - 576
  • [34] Color Features Performance Comparison for Image Retrieval
    Borghesani, Daniele
    Grana, Costantino
    Cucchiara, Rita
    IMAGE ANALYSIS AND PROCESSING - ICIAP 2009, PROCEEDINGS, 2009, 5716 : 902 - 910
  • [35] Combining color and texture features for image retrieval
    Wang, Guiting
    Tian, Baobao
    Jiao, Licheng
    MIPPR 2007: AUTOMATIC TARGET RECOGNITION AND IMAGE ANALYSIS; AND MULTISPECTRAL IMAGE ACQUISITION, PTS 1 AND 2, 2007, 6786
  • [36] Distributed Image Retrieval with Color and Keypoint Features
    Lagiewka, Michal
    Korytkowski, Marcin
    Scherer, Rafal
    2017 IEEE INTERNATIONAL CONFERENCE ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA), 2017, : 45 - 50
  • [37] Research on Image Retrieval Algorithm Based on Combination of Color and Shape Features
    Xiong, Zenggang
    Tang, Zhiwen
    Chen, Xiaowen
    Zhang, Xue-min
    Zhang, Kaibin
    Ye, Conghuan
    JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 2021, 93 (2-3): : 139 - 146
  • [38] Content Based Image Retrieval using Statistical Features of Color Histogram
    Varish, Naushad
    Pal, Arup Kumar
    2015 3RD INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATION AND NETWORKING (ICSCN), 2015,
  • [39] An Effective Visual Descriptor Based on Color and Shape Features for Image Retrieval
    Fierro-Radilla, Atoany
    Perez-Daniel, Karina
    Nakano-Miyatakea, Mariko
    Perez-Meana, Hector
    Benois-Pineau, Jenny
    HUMAN-INSPIRED COMPUTING AND ITS APPLICATIONS, PT I, 2014, 8856 : 336 - 348
  • [40] A fast and efficient image retrieval system based on color and texture features
    Singh, Chandan
    Kaur, Kanwal Preet
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2016, 41 : 225 - 238