Image Matching Based on Trusted Feature in Sketch-based Retrieval

被引:0
|
作者
Wu, Jian-jie [1 ]
Wu, Tao [1 ]
Yue, Yan [1 ]
Li, Xiao-long [1 ]
Tan, Yan-jie [1 ]
Wang, Xiao-chen [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Software Engn, Wuhan 430074, Peoples R China
关键词
sketch-based image retrieval; trusted feature; trusted feature distance; matching feature density; average normalized modified retrieval rank; DISTANCE TRANSFORMATIONS;
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
A novel image matching algorithm based on trusted feature in sketch-based retrieval is proposed. On the basis of traditional distance transformation (DT), trusted feature distance is introduced to eliminate those invalid distance values falsely contributed to the similarity calculation between sketch and database images, i.e. non-feature pixels which are actually far away from feature pixels will be ignored in similarity computing. Moreover, image similarity is weighted by matching feature density of the original color image to weaken the interference of a few highly matched pixels on the image similarity. Extensive experiments on various retrieval tasks show better accuracy than traditional DT methods.
引用
收藏
页码:14 / 18
页数:5
相关论文
共 50 条
  • [41] Academic Coupled Dictionary Learning for Sketch-based Image Retrieval
    Xu, Dan
    Alameda-Pineda, Xavier
    Song, Jingkuan
    Ricci, Elisa
    Sebe, Nicu
    MM'16: PROCEEDINGS OF THE 2016 ACM MULTIMEDIA CONFERENCE, 2016, : 1326 - 1335
  • [42] Fine-Grained Color Sketch-Based Image Retrieval
    Xia, Yu
    Wang, Shuangbu
    Li, Yanran
    You, Lihua
    Yang, Xiaosong
    Zhang, Jian Jun
    ADVANCES IN COMPUTER GRAPHICS, CGI 2019, 2019, 11542 : 424 - 430
  • [43] Feature Fusion and Metric Learning Network for Zero-Shot Sketch-Based Image Retrieval
    Zhao, Honggang
    Liu, Mingyue
    Li, Mingyong
    ENTROPY, 2023, 25 (03)
  • [44] Energy-Guided Feature Fusion for Zero-Shot Sketch-Based Image Retrieval
    Hao Ren
    Ziqiang Zheng
    Hong Lu
    Neural Processing Letters, 2022, 54 : 5711 - 5720
  • [45] Generalising Fine-Grained Sketch-Based Image Retrieval
    Pang, Kaiyue
    Li, Ke
    Yang, Yongxin
    Zhang, Honggang
    Hospedales, Timothy M.
    Xiang, Tao
    Song, Yi-Zhe
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 677 - 686
  • [46] A BAG-OF-REGIONS APPROACH TO SKETCH-BASED IMAGE RETRIEVAL
    Hu, Rui
    Wang, Tinghuai
    Collomosse, John
    2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011,
  • [47] Cineast: A Multi-Feature Sketch-Based Video Retrieval Engine
    Rossetto, Luca
    Giangreco, Ivan
    Schuldt, Heiko
    2014 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2014, : 18 - 23
  • [48] Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
    Liu, Li
    Shen, Fumin
    Shen, Yuming
    Liu, Xianglong
    Shao, Ling
    30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, : 2298 - 2307
  • [49] Sketch4Image: a novel framework for sketch-based image retrieval based on product quantization with coding residuals
    Li, Qiang
    Han, Yahong
    Dang, Jianwu
    MULTIMEDIA TOOLS AND APPLICATIONS, 2016, 75 (05) : 2419 - 2434
  • [50] Data Augmentation via Photo-to-Sketch Translation for Sketch-based Image Retrieval
    Furuya, Takahiko
    Ohbuchi, Ryutarou
    TENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2018), 2019, 11069