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Volume 14 - 2018

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A Novel Image Retrieval Method with Saliency Feature Vector

Volume 14, Number 2, February 2018, pp. 223-231
DOI: 10.23940/ijpe.18.02.p4.223231

Junfeng Wua,b, Wenyu Quc,*, Zhiyang Lid, Changqing Jie

aSchool of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

bSchool of Information Engineering, Dalian Ocean University, Dalian, 116023, China,

cSchool of Software, Tianjin University, Tianjin, 300072, China

dSchool of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

eSchool of Physical Science and Technology, Dalian University, Dalian, 116023, China


In the past few years, image retrieval has been one of the research focuses in the field of computer vision. For most retrieval methods, the accuracy of the retrieval results mainly depends on the extracted feature vectors. But, the foreground and the background in the images are not distinguished for most methods. It is obvious that these methods are not in accordance with the visual characteristics of the human eye. In this paper, salient objects are extracted from images in order to improve the pertinence of feature vector extraction. The paper utilizes a spatial pyramid model to divide the image into different parts with different scales. The feature vectors extracted in different scale are connected. Then, the saliency map and saliency score are used to rebuild the joint vector. Each feature vector is assigned different weighted values according to its different location in the image and scale. Finally, the newly constructed feature vectors are used to measure the similarity between images. In order to test the effectiveness of the algorithm, we evaluate our method on the SIMPLIcity dataset and Stanford dataset. Experimental results show that the proposed method has a great improvement in both accuracy and efficiency.


References: 14

  1. N. Ali, Bajwa K. B., Sablatnig R. "Image Retrieval by Addition of Spatial Information based on Histograms of Triangular Regions", Computers and Electrical Engineering,2016,54:539-550.
  2. M. Brown, R. Szeliski. "Multi-image Feature Matching Using Multi-scale Oriented Patches", IEEE, US7382897[P].2008.
  3. R. Fu, B. Li, Y. Gao. "Content-based Image Retrieval based on CNN and SVM", IEEE International Conference on Computer and Communications.IEEE,2017:638-642.
  4. T. Harada, Y. Ushiku, Y. Yamashita. "Discriminative Spatial Pyramid. "IEEE Computer Vision and Pattern Recognition,2011:1617-1624.
  5. S. Lazebnik, C. Schmid, J. Ponce." Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories", IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2006:2169-2178.
  6. X. Li, "Image Retrieval based on Perceptive Weighted Color Blocks", Pattern Recognition Letters,2003,24(12):1935-1941
  7. C. Kavitha, B. P. Rao, A. Govardhan. "Image Retrieval Based on Color and Texture Features of the Image Sub-blocks ". International Journal of Computer Applications,2011,15(7):33-37.
  8. B. Ko, H. Lee, H. Byun. "Image Retrieval Using Flexible Image Subblocks", ACM Symposium on Applied Computing. ACM, 2000: 574-578.
  9. H. Nishiki, S. Wada. "Robust Similar Image Retrieval Based on Extracted Object Features", Journal of Signal Processing, 2017, 21 (4):203-206.
  10. M. Ran, A. Tal, L. Zelnikmanor. "What Makes a Patch Distinct?", IEEE Conference on Computer Vision and Pattern Recognition. 2013:1139-1146.
  11. Suryanto, D. Kim, H. Kim. "Spatial Color Histogram based Center Voting Method for Subsequent Object Tracking and Segmentation", Image and Vision Computing,2011,29(12):850-860.
  12. J. Yang, J. C. Wang. "Color Histogram Image Retrieval based on Spatial and Neighboring Information", Computer Engineering and Applications,2007,43(27):158-160.
  13. W. Yu, K. Yang, H. Yao. "Exploiting the Complementary Strengths of Multi-layer CNN Features for Image Retrieval”, Neurocomputing, 2017,237:235-241.
  14. E. Vimina, K. Jacob. "A Sub-block Based Image Retrieval Using Modified Integrated Region Matching", International Journal of Computer Science Issues,2013,10(1).


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