Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 15 - 2019

No.1 January 2019
No.1 January 2019
No.2 February 2019
No.2 February 2019
No.3 March 2019
No.3 March 2019
No.4 April 2019
No.4 April 2019
No.5 May 2019
No.5 May 2019

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018
No.7 July 2018
No.7 July 2018
No.8 August 2018
No.8 August 2018
No.9 September 2018
No.9 September 2018
No.10 October 2018
No.10 October 2018
No.11 November 2018
No.11 November 2018
No.12 December 2018
No.12 December 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

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


Abstract:

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).

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Attachments:
Download this file (IJPE-2018-02-04.pdf)IJPE-2018-02-04.pdf[A Novel Image Retrieval Method with Saliency Feature Vector]553 Kb
 

CURRENT ISSUE

Prev Next

Cascaded Trust Network-based Block-Incremental Recommendation Strategy

Shujuan Ji, Da Li, Qing Zhang, Chunjin Zhang, and Chunxiao Bao

Read more

Cuckoo-based Malware Dynamic Analysis

Lele Wang, Binqiang Wang, Jiangang Liu, Qiguang Miao, and Jianhui Zhang

Read more

Colorization for Anime Sketches with Cycle-Consistent Adversarial Network

Guanghua Zhang, Mengnan Qu, Yuhao Jin, and Qingpeng Song

Read more

Bayesian Network Model for Learning Arithmetic Concepts

Yali Lv, Tong Jing, Yuhua Qian, Jiye Liang, Jianai Wu, and Junzhong Miao

Read more

Collaboration System Design of the Transportation Platform

Zhongwen Wang, Dong Liang, Ruizhen Duan, and Mingshan Chi

Read more

Specific Emitter Identification based on Power Amplifier

Zhen Zhang, Jie Chang, Mengqiu Chai, and Nan Tang

Read more

NRSSD: Normalizing Received Signal Strength to Address Device Diversity Problem in Fingerprinting Po…

Chunxiu Li, Jianli Zhao, Qiuxia Sun, Xiang Gao, Guoqiang Sun, and Chendi Zhu

Read more

Fast AIS Data Decoding Algorithm for Multi-Core CPU

Xiangkun Zeng, Huaran Yan, Yingjie Xiao and Xiaoming Yang

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com