An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification

被引:30
|
作者
Wang, Xiang-Yang [1 ,2 ]
Li, Yong-Wei [1 ]
Yang, Hong-Ying [1 ]
Chen, Jing-Wei [1 ]
机构
[1] Liaoning Normal Univ, Sch Comp & Informat Technol, Dalian 116029, Peoples R China
[2] Nanjing Univ Sci & Technol, Jiangsu Key Lab Image & Video Understanding Socia, Nanjing 210094, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Content-based image retrieval; Relevance feedback; Support vector machine; Orthogonal complement component analysis; Feature reconstruction;
D O I
10.1016/j.neucom.2013.08.007
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In content-based image retrieval (CBIR), the gap between low-level visual features and high-level semantic meanings usually leads to poor performance, and relevance feedback (RF) is an effective method to bridge this gap and to scale up the performance in CBIR systems. In recent years, the support vector machine (SVM) based relevance feedbacks have been popular because they can outperform many other classifiers when the size of the training set is small, but they are often very complex and some unsatisfactory relevance of results occur frequently. To overcome the above limitations, we propose a SVM relevance feedback CBIR algorithm based on feature reconstruction, in which the covariance matrix based kernel empirical orthogonal complement component analysis is utilized. Firstly, the original input image space is projected nonlinearly onto a high-dimensional feature space by using nonlinear analysis approaches. Secondly, the covariance matrix of the positive feedback images are calculated, and the kernel empirical orthogonal complement components of the covariance matrix are also calculated. Thirdly, the new features of positive feedback images, negative feedback images, and all the remaining images are reconstructed by utilizing the kernel empirical orthogonal complement components of positive feedback images. Finally, a SVM classifier is constructed and all the images are resorted based on the new reconstructed image feature. Experiments on large databases show that the proposed algorithms are significantly more effective than the state-of-the-art approaches. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:214 / 230
页数:17
相关论文
共 50 条
  • [1] Efficient relevance feedback scheme based on SVM in image retrieval
    Zhou, Jianxin
    Gao, Ke
    Li, Jintao
    Zhang, Yongdong
    Tang, Sheng
    [J]. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2007, 19 (04): : 535 - 540
  • [2] Application of SVM Relevance Feedback Algorithms in Image Retrieval
    Wang, Xuejun
    Yang, Lingling
    [J]. ISISE 2008: INTERNATIONAL SYMPOSIUM ON INFORMATION SCIENCE AND ENGINEERING, VOL 1, 2008, : 210 - 213
  • [3] Fuzzy SVM ensembles for relevance feedback in image retrieval
    Rao, Yong
    Mundur, Padma
    Yesha, Yelena
    [J]. IMAGE AND VIDEO RETRIEVAL, PROCEEDINGS, 2006, 4071 : 350 - 359
  • [4] IMAGE RETRIEVAL WITH FEATURE SELECTION AND RELEVANCE FEEDBACK
    Sun, Yu
    Bhanu, Bir
    [J]. 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 3209 - 3212
  • [5] A generalized relevance feedback scheme for image retrieval
    Zhou, XS
    Huang, TS
    [J]. INTERNET MULTIMEDIA MANAGEMENT SYSTEMS, 2000, 4210 : 348 - 355
  • [6] A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function
    Wang, Xiang-Yang
    Liang, Lin-Lin
    Li, Wei-Yi
    Li, Dong-Ming
    Yang, Hong-Ying
    [J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2016, 38 : 256 - 275
  • [7] Image retrieval: Feature primitives, feature representation, and relevance feedback
    Zhou, XS
    Huang, TS
    [J]. IEEE WORKSHOP ON CONTENT-BASED ACCESS OF IMAGE AND VIDEO LIBRARIES, PROCEEDINGS, 2000, : 10 - 14
  • [8] Joint semantics and feature based image retrieval using relevance feedback
    Lu, Y
    Zhang, HJ
    Liu, WY
    Hu, CH
    [J]. IEEE TRANSACTIONS ON MULTIMEDIA, 2003, 5 (03) : 339 - 347
  • [9] Random sampling based SVM for relevance feedback image retrieval
    Tao, DC
    Tang, XO
    [J]. PROCEEDINGS OF THE 2004 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 2, 2004, : 647 - 652
  • [10] Application of SVM-Based Relevance Feedback in Image Retrieval
    Wu, Xian Wei
    Yu, Wen Yang
    Yang, Yu Bin
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND ENGINEERING INNOVATION, 2015, 12 : 1072 - 1076