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2008 Fourth International Conference on Networked Computing and Advanced Information Management
Sum of Values of Local Histograms for Image Retrieval
September 02-September 04
ISBN: 978-0-7695-3322-3
| ASCII Text | x | ||
| Waqas Rasheed, Gwangwon Kang, Jinsuk Kang, Jonghun Chun, Jongan Park, "Sum of Values of Local Histograms for Image Retrieval," Networked Computing and Advanced Information Management, International Conference on, vol. 2, pp. 690-694, 2008 Fourth International Conference on Networked Computing and Advanced Information Management, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/NCM.2008.91, author = {Waqas Rasheed and Gwangwon Kang and Jinsuk Kang and Jonghun Chun and Jongan Park}, title = {Sum of Values of Local Histograms for Image Retrieval}, journal ={Networked Computing and Advanced Information Management, International Conference on}, volume = {2}, year = {2008}, isbn = {978-0-7695-3322-3}, pages = {690-694}, doi = {http://doi.ieeecomputersociety.org/10.1109/NCM.2008.91}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Networked Computing and Advanced Information Management, International Conference on TI - Sum of Values of Local Histograms for Image Retrieval SN - 978-0-7695-3322-3 SP690 EP694 A1 - Waqas Rasheed, A1 - Gwangwon Kang, A1 - Jinsuk Kang, A1 - Jonghun Chun, A1 - Jongan Park, PY - 2008 KW - Local Properties KW - CBIR KW - Histogram bins KW - Grayscale KW - Multimedia VL - 2 JA - Networked Computing and Advanced Information Management, International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/NCM.2008.91
CBIR makes a wide use of histogram based methods for image indexing. Histograms describe the global intensity distribution of images. They are very easy to compute and are insensitive to small changes in object translations and rotations. However, they are not robust to large appearance changes, and they might give similar results for different kinds of images if the distributions of colors are same in the images. Our research focuses mainly on the image bins (histogram value divisions by frequency) separation technique followed by calculating the sum of values, and using them as image local features. At first, the histogram is first calculated for an image. After that, it is subdivided into sixteen equal bins and the sum of local values is calculated and stored. We have tested the proposed algorithm on a large database of images.
Index Terms:
Local Properties, CBIR, Histogram bins, Grayscale, Multimedia
Citation:
Waqas Rasheed, Gwangwon Kang, Jinsuk Kang, Jonghun Chun, Jongan Park, "Sum of Values of Local Histograms for Image Retrieval," ncm, vol. 2, pp.690-694, 2008 Fourth International Conference on Networked Computing and Advanced Information Management, 2008
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