i-manager Publications

An Effective Image Retrieval Technique Based on Color and Texture to salient features of image

T. Ramashri*, K. prasanthi**

Professor, S.V. University, Tirupathi.

M.Tech, S.V. University, Tirupathi

Periodicity:November - January'2013
DOI : https://doi.org/10.26634/jcs.2.1.2075

Abstract

In content-based image retrieval (CBIR), color and texture are the most intuitive image features and it is widely used. But the current color feature can describe the semantics of the whole image effectively, but does not reflect characteristics of the color salience objects in an image. For the purpose of giving paper proposes a new color feature description model is proposed at first. This model integrates the intensity, the color contrast and self-saliency, sparsity and centricity saliency to describe human color visual perception of the image. Then, the new color feature descriptor is calculated by weighting the significant bit-plane histograms with color perception map. Finally, similarity measure is presented for the new color feature. Then for more efficient retrieval again the retrieved images are then compared for texture .Now because of the texture of salience retrieved image more accurate images can be retrieved. Experiment results show that the proposed color and texture feature is more accurate and efficient in retrieving images with user-interested color objects. Here a typical query can be a region of interest provided by the user, such as outlining patch in satellite image. Compared with the other retrieval methods, the proposed technique improves the retrieval accuracy effectively.

Keywords

How to Cite this Article?

Ramashri, T. and Prasanthi, K. (2013). An Effective Image Retrieval Technique Based on Color and Texture to Salient Features of Image. i-manager’s Journal on Communication Engineering and Systems, 2(1), 32-38. https://doi.org/10.26634/jcs.2.1.2075

References

[1]. Swain, M. J., & Ballard, D. H..(1991). Color indexing. International Journal of Computer Vision, 7(1), pp.11–32).

[2]. Stricker, M. A. & Orengo, M. (1995). Similarity of color images. In Proceedings of SPIE on storage retrieval for image and video databases, California, Vol. 2420, pp. 381-392).

[3]. Gao Li-chun, (2011). Xu Ye-qiang. Image retrieval based on relevance feedback using blocks weighted dominant colors in MPEG-7, Journal of computer applications, (31(6),pp.1549-1551).

[4]. Wang Xiang-Yang, Yang Hong-Ying, Zheng Hong- Liang, Wu Jun-Feng, (2010). A Color Block-histogram Image Retrieval Based on Visual Weight, Acta Automatica Sinica ,( 36(10),pp.1489-1492).

[5]. Wang Xiangyang, Hu Fengli (2007). A Robust Color Image Based on Significant Bit-plane, Journal of Image and Graphics, (12(9),pp.1647-1652).

[6]. Shen Yuntao (2005). Research on Visual Perception- Based Image Retrieval, Northwestern Polytechnical University, Dissertation for the Doctoral Degree in Pattern Analysis and Intelligent System, (pp.59-78).

[7]. C. Koch and S. Ulman (1985). Shifts in Selection in Visual Attention: Toward the Underlying Neural Circuitry. Human Neurobiology, (Vol. 4, No. 4, pp.219~227).

[8]. L. Itti, C. Koch, E. Niebur (1998). A model of saliencybased visual attention for rapid scene analysis [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, (20(11): pp.1254-1259).

[9] L. Itti, C. Koch (1999). A comparison of feature combination strategies for saliency-based visual attention systems [C]. Proceedings of Conference on Human Vision and Electronic Imaging IV SPIE, ( Vol. 3644: pp.373-382).

[10]. L. Itti, C. Koch (2001). Feature combination strategies for saliency-based visual attention systems [J]. Journal of Electronic Imaging, 10(1):pp.161-169).

[11]. L. Itti, C. Koch (2001). Computational modeling of visual attention [J]. Nature Reviews Neuroscience, (2(3):pp.194-230).

[12]. Itten J. (1961). The Elements of Color. John Wiley & Sons Inc., New York, USA.

[13]. A.C. Bovic, M. Clark, and W.S. Geisler, (1990). ''Multichannel Texture Anlysis Using Localized Spatial Filters,''IEEE Trans. Pattern Analysis and Machine Intelligence, (Vol. 12, No.1 ,pp.55-73,Jan).

[14]. B.S.Manjunath and R.Chellapa, (1993). "A unified Approach to Boundary Detection ,"IEEE Trans. Neural Networks, (Vol. 4, No. 1, pp 96-108, Jan).

[15]. B.S. Manjunath and R. Chellapa, (1992). "A Feature Based Approuch to Face Recognition," Proc. IEEE conf. CVPR'92, (pp.373-378, Chapaign,Ill., June).

[16]. B.S. Manjunath, C. Shekhar, and R. Chellapa, (1996). "A New Approach to Image Feature Detection with Applications,"Pattern Recognition, Apr.

[17]. T. Chang and C.C.J. Kuo, (1993). "Texture Analysis and classification with Tree-structured wavelet Transform,"IEEE Trns. Image processing, (vol.2, no.4,pp.429-441,Oct.).

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.