Abstract
Content Based Image Retrieval (CBIR) is one of the fastest growing research areas in the domain of multimedia. Due to the increase in digital contents these days, users are experiencing difficulties in searching for specific images in their databases. This paper proposed a new effective and efficient image retrieval technique based on color histogram using Hue-Saturation-Value (HSV) and First Order Statistics (FOS), namely HSV-fos. FOS is used for the extraction of texture features while color histogram deals with color information of the image. Performance of the proposed technique is compared with the Variance Segment and Histogram based techniques and results shows that HSV-fos technique achieved 15% higher accuracy as compared to Variance Segment and Histogram-based techniques. The proposed technique can help the forensic department for identification of suspects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Deshmukh, A., Phadke, G.: An improved content based image retrieval. In: 2nd International Conference on Computer and Communication Technology (ICCCT), pp. 191–195 (September 2011)
Sheikh, A., Lye, M., Mansor, S., Fauzi, M., Anuar, F.: A content based image retrieval system for marine life images. In: IEEE 15th International Symposium on Consumer Electronics (ISCE), pp. 29–33 (June 2011)
Gnaneswara Rao, K.N., Vijaya, V., Venkata, K.V.: Texture based image indexing and retrieval. IJCSNS International Journal of Computer Science and Network Security 9, 206–210 (2009)
Abubacker, K., Indumathi, L.: Attribute associated image retrieval and similarity re ranking. In: International Conference on Communication and Computational Intelligence (INCOCCI), pp. 235–240 (December 2010)
Imran, M., Hashim, R., Abd Khalid, N.: New approach to image retrieval based on color histogram. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013, Part II. LNCS, vol. 7929, pp. 453–462. Springer, Heidelberg (2013)
Broilo, M., De Natale, F.G.B.: A stochastic approach to image retrieval using relevance feedback and particle swarm optimization. IEEE Transactions on Multimedia 12(4), 267–277 (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Imran, M., Hashim, R., Khalid, N.: Opposition based particle swarm optimization with student t mutation (ostpso). In: 4th Conference on Data Mining and Optimization (DMO), pp. 80–85 (2012)
Imran, M., Manzoor, Z., Ali, S., Abbas, Q.: Modified particle swarm optimization with student t mutation (stpso). In: International Conference on Computer Networks and Information Technology (ICCNIT), pp. 283–286 (2011)
Imran, M., Hashim, R., Khalid, N.E.A.: An overview of particle swarm optimization variants. Procedia Engineering 53, 491–496 (2013)
Huang, Z.-C., Chan, P., Ng, W., Yeung, D.: Content-based image retrieval using color moment and gabor texture feature. In: International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 719–724 (July 2010)
Rafael, R.E.W., Gonzalez, C., Eddins, S.L.: Digital image processing using matlab. Publishing House of Electronics Industry (2009)
Selvarajah, S., Kodituwakku, S.R.: Analysis and comparison of texture features for content based image retrieval. International Journal of Latest Trends in Computing 2, 108–113 (2011)
Materka, A., Strzelecki, M.: Texture analysis methods a review. Technical University of Lodz, Institute of Electronics (1998)
Bhuravarjula, H., Kumar, V.: A novel content based image retrieval using variance color moment. International Journal of computer and Electronic Research 1, 93–99 (2012)
Rubner, Y., Guibas, L.J., Tomasi, C.: The earth mover’s distance, multi-dimensional scaling, and color-based image retrieval. In: Proceedings of the ARPA Image Understanding Workshop, pp. 661–668 (1997)
Banerjee, M., Kundu, M.K., Maji, P.: Content-based image retrieval using visually significant point features. Fuzzy Sets and Systems 160, 3323–3341 (2009)
Hiremath, P., Pujari, J.: Content based image retrieval using color boosted salient points and shape features of an image. International Journal of Image Processing 2(1), 10–17 (2008)
Wang, J., Li, J., Wiederhold, G.: Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 947–963 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Imran, M., Hashim, R., Khalid, N.E.A. (2014). Color Histogram and First Order Statistics for Content Based Image Retrieval. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-07692-8_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07691-1
Online ISBN: 978-3-319-07692-8
eBook Packages: EngineeringEngineering (R0)