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Image Retrieval Using Histogram Refinement Based on Local Color Difference
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 Title & Authors
Image Retrieval Using Histogram Refinement Based on Local Color Difference
Kim, Min-KI;
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 Abstract
Since digital images and videos are rapidly increasing in the internet with the spread of mobile computers and smartphones, research on image retrieval has gained tremendous momentum. Color, shape, and texture are major features used in image retrieval. Especially, color information has been widely used in image retrieval, because it is robust in translation, rotation, and a small change of camera view. This paper proposes a new method for histogram refinement based on local color difference. Firstly, the proposed method converts a RGB color image into a HSV color image. Secondly, it reduces the size of color space from 2563 to 32. It classifies pixels in the 32-color image into three groups according to the color difference between a central pixel and its neighbors in a 3x3 local region. Finally, it makes a color difference vector(CDV) representing three refined color histograms, then image retrieval is performed by the CDV matching. The experimental results using public image database show that the proposed method has higher retrieval accuracy than other conventional ones. They also show that the proposed method can be effectively applied to search low resolution images such as thumbnail images.
 Keywords
Image Retrieval;Local Color Difference;Histogram Refinement;
 Language
Korean
 Cited by
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Ferns 알고리즘 기반 밝기 및 회전 변화에 강인한 영상검색 시스템 설계 및 구현,윤석환;심재성;박석천;

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동일인 인식을 위한 컬러 공간의 탐색 및 결합,남영호;김민기;

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 References
1.
X. Bai, B. Wang, C. Yao, W. Liu, and Z. Tu, “Co-Transduction for Shape Retrieval,” IEEE Transactions on Image Processing, Vol. 21, No. 5, pp. 2747-2757, 2012. crossref(new window)

2.
A. Egozi, Y. Keller, and H. Guterman, “Improving Shape Retrieval by Spectral Matching and Meta Similarity,” IEEE Transactions on Image Processing, Vol. 19, No. 5, pp. 1319-1327, 2010. crossref(new window)

3.
A.K. Jain and A. Vailaya, “Shape-Based Retrieval: A Case Study with Trademark Image Databases,” Pattern Recognition, Vol. 31, No. 9, pp. 1369-1390, 1998. crossref(new window)

4.
G. Pass and R. Zabih, "Histogram Refinement for Content-Based Image Retrieval," Proceeding of the IEEE Workshop on Applications of Computer Vision, pp. 96-102, 1996.

5.
J. Huang, S.R. Kumar, M. Mitra, W. Zhu, and R. Zabih, "Image Indexing Using Color Correlograms," Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762-768, 1997.

6.
K. Kang, Y. Park, Y. Yoon, J. Choi, and D. Kim, “Image Retrieval using Spatial Information and Color Changing Ratio,” Journal of Korea Multimedia Society, Vol. 11, No. 1, pp. 23-33, 2008.

7.
P. Haldar and J. Mukherjee, “Content based Image Retrieval Using Histogram, Color and Edge,” International Journal of Computer Application, Vol. 48, No. 11, pp. 25-31, 2012. crossref(new window)

8.
C. Won, D. Park, and S. Park, “Efficient Use of MPEG-7 Edge Histogram Descriptor,” Journal of Electronics and Telecommunications Research Institute, Vol. 24, No. 1, pp. 23-30, 2002.

9.
V. Takala, T. Ahonen, and M. Pietikainen, "Block-Based Methods for Image Retrieval Using Local Binary Patterns," Lecture Notes in Computer Science, Vol. 3540, pp. 882-891, 2005.

10.
J. Song, “Content-based Image Retrieval Using HSV Color and Uniform Local Binary Pattern,” Journal of Korean Institute of Information Technology, Vol. 12, No. 6, pp. 169-174, 2014. crossref(new window)

11.
K. Lee and C. Lee, “Content-based Image Retrieval Using LBP and HSV Color Histogram,” Journal of Broadcast Engineering, Vol. 18, No. 3, pp. 372-379, 2013. crossref(new window)

12.
S. Murala, R.P. Maheshwari, and R. Balasubramanian, “Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval,” IEEE Transactions on Image Processing, Vol. 21, No. 5, pp. 2874-2886, 2012. crossref(new window)

13.
R. Balasubramani and V. Kannan, “Efficient Use of MPEG-7 Color Layout and Edge Histogram Descriptors in CBIR Systems,” Global Journal of Computer Science and Technology, Vol. 9, No. 5, pp. 157-163, 2009.

14.
H.A. Jalab, "Image Retrieval System based on Color Layout Descriptor and Gabor Filters," Proceeding of the IEEE Conference on Open Systems, pp. 32-36, 2011.

15.
M.H. Saad, H.I. Saleh, H. Konbor, and M. Ashour, "Image Retrieval based on Integration between YCbCr Color Histogram and Texture Feature," International Journal of Computer Theory and Engineering, Vol. 3, No. 5, pp. 701-706, 2011. crossref(new window)

16.
M. Singh and K. Hemachandran, "Content Based Image Retrieval using Color and Texture," Signal & Image Processing : An International Journal, Vol. 3, No. 1, pp. 39-57, 2012. crossref(new window)

17.
S.M. Singh and K. Hemachandran, "Content-Based Image Retrieval using Color Moment and Gabor Texture Feature," International Journal of Computer Science Issues, Vol. 9, Issue 5, No. 1, pp. 299-309, 2012.

18.
M. Mustikasari, S. Madenda, E. Prasetyo, D. Kerami, and S. Harmanto, “Content-Based Image Retrieval using Local Color Histogram,” International Journal of Engineering Research, Vol. 3, No. 8, pp. 507-511, 2014. crossref(new window)

19.
M. Stricker and A. Dimai, "Color Indexing with Weak Spatial Constraints," Proceeding of the Storage Retrieval for Still Image and Video Databases, pp. 1-12, 1996.

20.
R.O. Stehling, M.A. Nascimento, and A.X. Falcao, “Cell Histograms Versus Color Histograms for Image Representation and Retrieval,” Knowledge and Information Systems, Vol. 5, No. 3, pp. 315-336, 2003. crossref(new window)

21.
James Z. Wang's Research Group, http://wang.ist.psu.edu/docs/related.shtml (accessed Nov., 1, 2015).