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Content-based Image Retrieval using Visual Attention Point Features

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the challenges in the development of a content-based image indexing and retrieval application is to achieve an efficient and robust indexing scheme. Color is a fundamental image feature used in content-based image retrieval (CBIR) systems. This paper proposes a robust and effective image retrieval scheme, which is based on the weighed color histogram of visual attention points. Firstly, the fully affine invariant visual attention points are extracted from the origin color image by using the Affine-SIFT (scale-invariant feature transform) detector. Secondly, according to the color complexity measure (CCM) theory, the visual weight values for the significant visual attention points are calculated to reflect the image local variation. Then, the weighed color histogram of visual attention points is constructed. Finally, the similarity between color images is computed by using the weighed color histogram of visual attention points. Experimental results show that the proposed image retrieval is not only more accurate and efficient in retrieving the user-interested images, but also yields higher retrieval accuracy than some state-of-the-art image retrieval schemes for various test DBs.
Wydawca
Rocznik
Strony
309--329
Opis fizyczny
Bibliogr. 31 poz., fot., wykr.
Twórcy
autor
  • School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P. R. China
autor
  • School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P. R. China
autor
  • School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P. R. China
autor
  • School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P. R. China
autor
  • School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, P. R. China
Bibliografia
  • [1] Weiming Hu, Nianhua Xie, Li Li, Xianglin Zeng, and Stephen J. Maybank. A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 41(6):797–819, 2011.
  • [2] Ritendra Datta, Dhiraj Joshi, Jia Li, and James Z. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv., 40(2):5:1–5:60, May 2008.
  • [3] Otávio Augusto Bizetto Penatti, Eduardo Valle, and Ricardo da Silva Torres. Comparative study of global color and texture descriptors for web image retrieval. J. Visual Communication and Image Representation, 23(2):359–380, 2012.
  • [4] Thomas Deselaers, Daniel Keysers, and Hermann Ney. Features for image retrieval: an experimental comparison. Inf. Retr., 11(2):77–107, 2008.
  • [5] Michael J. Swain and Dana H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11–32, 1991.
  • [6] Wankou Yang, Changyin Sun, and Lei Zhang. A multi-manifold discriminant analysis method for image feature extraction. Pattern Recognition, 44(8):1649–1657, 2011.
  • [7] Guang-Hai Liu and Jing-Yu Yang. Content-based image retrieval using color difference histogram. Pattern Recognition, 46(1):188–198, 2013.
  • [8] P.-T. Yap and R. Paramesran. Content-based image retrieval using legendre chromaticity distribution moments. IEE Proceedings-Vision, Image and Signal Processing, 153(1):17–24, 2006.
  • [9] Xiangyang Wang, Yong-Jian Yu, and Hong-Ying Yang. An effective image retrieval scheme using color, texture and shape features. Computer Standards & Interfaces, 33(1):59–68, 2011.
  • [10] Wei-Ta Chen, Wei-Chuan Liu, and Ming-Syan Chen. Adaptive color feature extraction based on image color distributions. IEEE Transactions on Image Processing, 19(8):2005–2016, 2010.
  • [11] Murala Subrahmanyam, R. P. Maheshwari, and R. Balasubramanian. Local tetra patterns: A new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing, 21(5):2874–2886, 2012.
  • [12] Xuelong Li. Image retrieval based on perceptive weighted color blocks. Pattern Recognition Letters, 24(12):1935–1941, 2003.
  • [13] Mu-Yeon Kim, Seok-Min Chae, Soo-Yeon Son, Young-Choon Kim, Sang-Ho Ahn, Min-Ho Park, and Kuhn-Il Lee. Image retrieval using block color characteristics and spatial pattern correlation. In IPCV, pages 173–179, 2006.
  • [14] Mohammad Faizal Ahmad Fauzi and Paul H. Lewis. Block-based against segmentation-based texture image retrieval. J. UCS, 16(3):402–423, 2010.
  • [15] Mann-Jung Hsiao, Yo-Ping Huang, and Te-Wei Chiang. A region-based image retrieval approach using block dct. In Innovative Computing, Information and Control, 2007. ICICIC’07. Second International Conference on, pages 218–221. IEEE, 2007.
  • [16] Yunqi Lei, Xiaoling Gui, and Zhenxiang Shi. Feature description and image retrieval based on visual attention model. Journal of Multimedia, 6(1):56–65, 2011.
  • [17] Sylvie Philipp-Foliguet, Julien Gony, and Philippe Henri Gosselin. Frebir: An image retrieval system based on fuzzy region matching. Computer Vision and Image Understanding, 113(6):693–707, 2009.
  • [18] Chi-Chou Kao, Yen-Tai Lai, and Chia-Hui Lin. An efficient reflection invariance region-based image retrieval framework. Int. J. Imaging Systems and Technology, 20(2):155–161, 2010.
  • [19] Chi-Han Chuang, Shyi-Chyi Cheng, and Chin-Chun Chang. Adaptive image segmentation for region-based object retrieval using generalized hough transform. Pattern Recognition, 43(10):3219–3232, 2010.
  • [20] Wei Huang, Yan Gao, and Kap Luk Chan. A review of region-based image retrieval. Signal Processing Systems, 59(2):143–161, 2010.
  • [21] Minakshi Banerjee, Malay Kumar Kundu, and Pradipta Maji. Content-based image retrieval using visually significant point features. Fuzzy Sets and Systems, 160(23):3323–3341, 2009.
  • [22] Julian Stöttinger, Nicu Sebe, Theo Gevers, and Allan Hanbury. Colour interest points for image retrieval. In Proceedings of the 12th Computer Vision Winter Workshop, pages 83–90, 2007.
  • [23] Neville Mehta, R. S. Alomari, and Vipin Chaudhary. Content based sub-image retrieval system for high resolution pathology images using salient interest points. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 3719–3722. IEEE, 2009.
  • [24] Anil K. Jain, Jung-Eun Lee, Rong Jin, and Nicholas Gregg. Content-based image retrieval: An application to tattoo images. In ICIP, pages 2745–2748, 2009.
  • [25] Xiangyang Wang, Jun-Feng Wu, and Hong-Ying Yang. Robust image retrieval based on color histogram of local feature regions. Multimedia Tools Appl., 49(2):323–345, 2010.
  • [26] Mahmoud Mejdoub, Leonardo H. Fonteles, Chokri Ben Amar, and Marc Antonini. Embedded lattices tree: An efficient indexing scheme for content based retrieval on image databases. J. Visual Communication and Image Representation, 20(2):145–156, 2009.
  • [27] Zhao Shan, Wang Shui, and Gao Guo Hong. Content-based image retrieval using salient points. In Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on, volume 1, pages 201–204. IEEE, 2010.
  • [28] Giang P. Nguyen and Hans Jørgen Andersen. Context-based adaptive filtering of interest points in image retrieval. In ISDA, pages 529–534, 2009.
  • [29] Guoshen Yu and Jean-Michel Morel. A fully affine invariant image comparison method. In ICASSP, pages 1597–1600, 2009.
  • [30] Jean-Michel Morel and Guoshen Yu. Asift: A new framework for fully affine invariant image comparison. SIAM J. Imaging Sciences, 2(2):438–469, 2009.
  • [31] Kuk-Jin Yoon and In-So Kweon. Color image segmentation considering the human sensitivity for color pattern variations. In Proc. SPIE, volume 4572, pages 269–278, 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9c7f8bd2-b029-4e14-b5ac-38719e3df92a
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