International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 7, No. 5, 8 Sep. 2015
Cover page and Table of Contents: PDF (size: 402KB)
An Approach for Similarity Matching and Comparison in Content based Image Retrieval System
Full Text (PDF, 402KB), PP.48-54
Views: 0 Downloads: 0
Author(s)
Index Terms
CBIR, HSV, Recall and Precision, Searching
Abstract
Today, in the age of images and digitization relevant retrieval is quite a topic of research. In past era, the database was having only text or database was low dimensional type. But with every new day thousands of pictures are getting added into the database making it a high dimensional data set. Therefore, from a high dimensional dataset to get a set of relevant images is quite a cumbersome task. Number of approaches for getting relevant retrieval is defined, some includes retrievals only on the basis of color, while some include more than one primitive feature to retrieve the relevant image such as color, shape and texture. In this paper experiment has been performed on the trademark images. Trademark is a very important asset for any organization and increasing trademark images have developed a quick need to organize these images. This paper includes the implementation of HSV model for fast retrieval. Which use color and texture so as to extract feature vector. Experiment takes query image and retrieve twelve most relevant images to the user. Further for performance evaluation parameter used is Precision and Recall.
Cite This Paper
Er. Numa Bajaj, Er. Jagbir Singh Gill, Rakesh Kumar, "An Approach for Similarity Matching and Comparison in Content based Image Retrieval System", IJIEEB, vol.7, no.5, pp.48-54, 2015. DOI:10.5815/ijieeb.2015.05.07
Reference
[1]Huang, J., Kumar, S. R., Mitra, M., Zhu, W. J., & Zabih, R. (1999). Spatial color indexing and applications. International Journal of Computer Vision, 35(3), 245-268.
[2]Hiremath, P. S., & Pujari, J. (2007, December). Content based image retrieval using color, texture and shape features. In Advanced Computing and Communications, 2007. ADCOM 2007. International Conference on (pp. 780-784). IEEE.
[3]Song, Y. J., Park, W. B., Kim, D. W., & Ahn, J. H. (2004, November). Content-based image retrieval using new color histogram. In Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004. P oceedings of 2004 International Symposium on (pp. 609-611). IEEE.
[4]Murala, S., Gonde, A. B., & Maheshwari, R. P. (2009, March). Color and texture features for image indexing and retrieval. In Advance Computing Conference, 2009. IACC 2009. IEEE International (pp. 1411-1416). IEEE.
[5]Kekre, H. B., & Sonawane, K. (2014, April). Comparative study of color histogram based bins approach in RGB, XYZ, Kekre's LXY and L′ X′ Y′ color spaces. In Circuits, Systems, Communication and Information Technology Applications (CSCITA), 2014 International Conference on (pp. 364-369). IEEE.
[6]Ketenci, S., & Gencturk, B. (2013, July). Performance analysis in common color spaces of 2D Gaussian Color Model for skin segmentation. InEUROCON, 2013 IEEE (pp. 1653-1657). IEEE.
[7]Manjunath, B. S., Ohm, J. R., Vasudevan, V. V., & Yamada, A. (2001). Color and texture descriptors. Circuits and Systems for Video Technology, IEEE Transactions on, 11(6), 703-715.
[8]Zhao, Q., Yang, J., Yang, J., & Liu, H. (2009, April). Stone images retrieval based on color histogram. In Image Analysis and Signal Processing, 2009. IASP 2009. International Conference on (pp. 157-161). IEEE.
[9]Yu, H., Li, M., Zhang, H. J., & Feng, J. (2002, June). Color texture moments for content-based image retrieval. In Image Processing. 2002. Proceedings. 2002 International Conference on (Vol. 3, pp. 929-932). IEEE.
[10]Arthi, K., & Vijayaraghavan, M. J. (2013). Content based image retrieval algorithm using colour models. International Journal of Advanced Research in Computer and Communication Engineering, 2(3), 1343-47.
[11]Kekre, H. B., & Sonawane, K. (2012). Histogram Partitioning for Feature Vector Dimension Reduction in Bins Approach for CBIR. IJECCE, 3(6), 1630-1639.
[12]Kekre, H. B., & Sonawane, K. (2013). Performance evaluation of bins approach in YCbCr color space with and without scaling. International Journal of Soft Computing and Engineering, 3(3), 203-210.
[13]Müller, W., Squire, D. M., Marchand-Maillet, S., & Pun, T. (2001). Performance evaluation in content-based image retrieval: overview and proposals. Pattern Recognition Letters, 22(5), 593-601.
[14]Sharma, N. S., Rawat, P. S., & Singh, J. S. (2011). Efficient CBIR using color histogram processing. Signal & Image Processing, 2(1).
[15]Suhasini, P. S., Krishna, K., & Krishna, I. M. (2009). CBIR USING COLOR HISTOGRAM PROCESSING. Journal of Theoretical & Applied Information Technology, 6(1).
[16]Jeong, S., Won, C. S., & Gray, R. M. (2004). Image retrieval using color histograms generated by Gauss mixture vector quantization. Computer Vision and Image Understanding, 94(1), 44-66.
[17]Datta, R., Li, J., & Wang, J. Z. (2005, November). Content-based image retrieval: approaches and trends of the new age. In Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval (pp. 253-262). ACM.
[18]Schettini, R., Ciocca, G., & Zuffi, S. (2001). A survey of methods for colour image indexing and retrieval in image databases. Color Imaging Science: Exploiting Digital Media, 183-211.