Effect of Reducing Colors Number on the Performance of CBIR System
Автор: Abbas H. Hassin Alasadi, Saba Abdual Wahid
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 9 vol.8, 2016 года.
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Taking inspiration from the fact that a human can distinguish only a limited number of colors, reducing the number of colors is an interesting task to be incorpo-rated in image retrieval systems that is based on using only the most discriminative colors, which most of the time yields better results. Accordingly, the main goal of this paper is to study the influence on performance of reducing the colors number contained in images. Accomplishing this task poses an extra overhead on the system, which requires more com-putation time, but, on the other hand, can accelerate the comparison process. Due to their popularity and success, we specifically concentrate this study on histogram in-dexing methods, using both Euclidean distance and histo-gram intersection to assess consequently the distance and the similarity between images. Some simple, pertinent ideas related to the way we compare a pair of images using Euclidean Distance are given in the end of the pa-per, supported by preliminary obtained results.
CBIR, image retrieval, histogram indexing, colors reduction, histogram intersection
Короткий адрес: https://sciup.org/15014010
IDR: 15014010
Список литературы Effect of Reducing Colors Number on the Performance of CBIR System
- Abbas H. Hassin and Ali B. Yousif (2013). Content-based Image Retrieval using Texture and Color Features. Journal of Thi-Qar Science, 3(4):142-149
- Barya, N., & Jaiswal, H. (2015). Survey on Content based Image Retrieval to Deal with Rapid Growth of Digital Images. International Journal of Computer Appli-cations, 124(12).
- Dharani, T., & Aroquiaraj, I. L. (2013, February). A sur-vey on content based image retrieval. In Pattern Recogni-tion, Informatics and Mobile Engineering (PRIME), 2013 International Conference on (pp. 485-490). IEEE.
- Flickner, M., Sawhney, H., Niblack, W., et al (1995). Query by image and video content: The QBIC sys-tem.Computer, 28(9), 23-32.
- Jaworska, T. (2016). Query Techniques for CBIR. In Flexible Query Answering Systems 2015 (pp. 403-416). Springer International Publishing.
- Ibrahim S. I. Abuhaiba,Ruba A. A. Salamah (2012). Effi-cient Global and Region Content Based Image Retrieval, IJIGSP, 4 (5),pp.38-46.
- Sharma, N. S., Rawat, P. S., & Singh, J. S. (2011). Effi-cient CBIR using color histogram processing. Signal & Image Processing, 2(1).
- Shrivastava, N., & Tyagi, V. (2015). An efficient tech-nique for retrieval of color images in large databases. Computers & Electrical Engineering, 46, 314-327.
- Juneja, K., Verma, A., Goel, S., & Goel, S. (2015, Febru-ary). A survey on recent image indexing and retrieval techniques for low-level feature extraction in CBIR sys-tems. In Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference on (pp. 67-72). IEEE.
- Singha, M., & Hemachandran, K. (2012). Content based image retrieval using color and texture. Signal & Image Processing: An International Journal (SIPIJ), 3(1), 39-57.
- Kekre, H. B., & Sonawane, K. (2013). Histogram Bins Matching Approach for CBIR Based on Linear grouping for Dimensionality Reduction. International Journal of Image, Graphics and Signal Processing, 6(1), 68.
- Jasmine, K. P., & Kumar, P. R. (2014). Color Histogram and DBC Co-Occurrence Matrix for Content Based Image Retrieval. International Journal of Information Engi-neering and Electronic Business (IJIEEB), 6(6), 47.
- Rahmani, M. K. I., Ansari, M. A., & Goel, A. K. (2015, February). An Efficient Indexing Algorithm for CBIR. In Computational Intelligence & Communication Tech-nology (CICT), 2015 IEEE International Conference on (pp. 73-77). IEEE.
- Liu, G. H., & Yang, J. Y. (2013). Content-based image retrieval using color difference histogram. Pattern Recognition, 46(1), 188-198.
- Patil, C. G., Kolte, M. T., Chatur, P. N., & Chaudhari, D. S. (2014). Optimum Features selection by fusion using Genetic Algorithm in CBIR. International Journal of Im-age, Graphics and Signal Processing (IJIGSP), 7(1), 25.
