Vol 7, No 4 (2016) > Electrical, Electronics and Computer Engineering >

Query Region Determination based on Region Importance Index and Relative Position for Region-based Image Retrieval

Pasnur Pasnur, Agus Zainal Arifin, Anny Yuniarti


Abstract: An efficient
Region-Based Image Retrieval (RBIR) system must consider query region
determination techniques and target regions in the retrieval process. A query region is a region
that must contain
a Region of Interest (ROI) or saliency region. A query region determination can be specified
manually or automatically. However, manual determination is considered less
efficient and tedious for users. The selected query region must determine specific
target regions in the image collection to reduce the retrieval time. This study
proposes a strategy of query region determination based on the Region
Importance Index (RII) value and relative position of the Saliency Region
Overlapping Block (SROB) to produce a more efficient RBIR. The entire region is
formed by using the mean shift segmentation method. The RII value is calculated
based on a percentage of the region area and region distance to the center of
the image. Whereas
the target regions are determined by considering the relative position of SROB,
the performance of the proposed method is tested on a CorelDB dataset.
Experimental results show that the proposed method can reduce the Average of
Retrieval Time to 0.054 seconds with a 5x5 block size configuration.
Keywords: Local binary pattern; Region-based image retrieval; Region importance index; Relative position; Region code; Saliency region

Full PDF Download


Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M., 2015. Global Contrast Based Salient Region Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 37(3), pp. 1–8

Lee, J., Nang, J., 2011. Content Based Image Retrieval Method using the Relative Location of Multiple ROIs. Advances in Electrical and Computer Engineering, Volume 11(3), pp. 85–90

Shete, D.S., Chavan, M.S., 2012. Content Based Image Retrieval : Review. International Journal of Emerging Technology and Advanced Engineering, Volume 2(9), pp. 85–90

Shrivastava, N., Tyagi, V., 2014. Content Based Image Retrieval based on Relative Locations of Multiple Regions of Interest using Selective Regions Matching. Information Sciences, Volume 259, pp. 212–224

Singh, B., Ahmad, W., 2014. Content Based Image Retrieval: A Review Paper. International Journal of Computer Science and Mobile Computing, Volume 3(5), pp. 769–775

Tao, W., Jin, H., Zhang, Y., 2007. Color Image Segmentation based on Mean Shift and Normalized Cuts. IEEE Transactions on Systems, MAN, and Cybernetics, Volume 37(5), pp. 1382–1389

Tian, Q.T.Q., Wu, Y.W.Y., Huang, T.S., 2000. Combine User Defined Region of Interest and Spatial Layout for Image Retrieval. In: Proceedings 2000 International Conference on Image Processing, Voume 3, pp. 746–749

Vimina, E.R., Jacob, K.P., 2013. A Sub-block based Image Retrieval using Modified Integrated Region Matching. International Journal of Computer Science Issues, Volume 10(1), pp. 686–692

Wang, X., Wang, Z., 2013. A Novel Method for Image Retrieval based on Structure Elements’ Descriptor. Journal of Visual Communication and Image Representation, Volume 24(1), pp. 63–74

Wang, X.Y., Yu, Y.J., Yang, H.Y., 2011. An Effective Image Retrieval Scheme using Color, Texture and Shape Features. Computer Standards & Interfaces, Volume 33(1), pp. 59–68

Yang, X., Cai, L., 2014. Adaptive Region Matching for Region-based Image Retrieval by Constructing Region Importance Index. IET Computer Vision, Volume 8(2), pp. 141–151

Zhu, C., Bichot, C.E., Chen, L., 2013. Image Region Description using Orthogonal Combination of Local Binary Patterns Enhanced with Color Information. Pattern Recognition, Voume 46(7), pp. 1949–1963