Abstract
Content based image retrieval involves extraction of global and region features of images for improving their retrieval performance in large image databases. Region based feature have shown to be more effective than global features as they are capable of reflecting users specific interest with greater accuracy. However success of region based methods largely depends on the segmentation technique used to automatically specify the region of interest (ROI) in the query. Apart from this user can also specify ROI’s in an image. The ROI image retrieval involves the task of formulation of region based query, feature extraction, indexing and retrieval of images containing similar region as specified in the query. In this paper state-of-the-art techniques for ROI image retrieval are discussed. Comparative study of each of these techniques together with pros and cons of each technique are listed. The paper is concluded with our views on challenges faced by researchers and further scope of research in the area. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image retrieval based on ROI.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wong, K.-M., Cheung, K.-W., Po, L.-M.: MIRROR: An Interactive Content Based Image Retrieval System. In: Proc. of IEEE Int. Symposium on Circuits and Systems (ISCAS 2005), vol. 2, pp. 1541–1544 (2005), http://dx.doi.org/10.1109/ISCAS.2005.1464894
Broek, E.L., Kisters, P.M.F., Vuurpijl, L.G.: The utilization of human color categorization for content-based image retrieval. In: Proc. of the SPIE, vol. 5292, pp. 351–362 (2004)
Tian, Q., Wu, Y., Huang, T.S.: Combine User Defined Region-of-Interest and Spatial Layout for Image Retrieval. In: Proc. of IEEE Int. Conf. on Image Processing (ICIP 2000), vol. 3, pp. 746–749 (2000), http://dx.doi.org/10.1109/ICIP.2000.899562
Prasad, B.G., Biswas, K.K., Gupta, S.K.: Region-Based Image Retrieval using Integrated Color, Shape and Location Index. In: Computer Vision and Image Understanding, vol. 94, pp. 193–233 (2004), http://dx.doi.org/10.1016/j.cviu.2003.10.016
Moghaddam, B., Biermann, H., Margaritis, D.: Regions-of-Interest and Spatial Layout for Content-Based Image Retrieval. Multimedia Tools and Applications 14(2), 201–210 (2001), http://dx.doi.org/10.1023/A:1011355417880
Chan, Y.-K., Ho, Y.-A., Liu, Y.-T., Chen, R.-C.: A ROI image retrieval method based on CVAAO. Image and Vision Computing 26, 1540–1549 (2008)
Lee, J., Nang, J.: Content-Based Image Retrieval Method using the Relative Location of Multiple ROIs. Advances in Electrical and Computer Engineering 11(3), 85–90 (2011)
Ko, B.C., Byun, H.: FRIP: A Region-Based Image Retrieval Tool Using Automatic Image Segmentation and Stepwise Boolean AND Matching. IEEE Transactions on Multimedia 7(1) (February 2005)
Vu, K., Hua, K.A., Tavanapong, W.: Image retrieval based on regions of interest. IEEE Transactions on Knowledge and Data Engineering 15(4), 1045–1049 (2003)
Zhang, J., Yoo, C.-W., Ha, S.-W.: ROI Based Natural Image Retrieval using Color and Texture Feature. Fuzzy Systems and Knowledge Discovery (2007)
Chen, T., Chen, L.-H., Ma, K.-K.: Colour Image Indexing Using SOM for Region-of-Interest Retrieval. Pattern Analysis & Applications 2, 164–171 (1999)
Zhou, Q., Ma, L., Celenk, M., Chelberg, D.: Content-Based Image Retrieval Based on ROI Detection and Relevace Feedback. Multimedia Tools and Application 27, 251–281 (2005)
Hsiao, M.-J., Huang, Y.-P., Tsai, T., Chiang, T.-W.: An Efficient and Flexible Matching Strategy for Content-based Image Retrieval. Life Science Journal 7(1) (2010)
Huang, C., Liu, Q., Yu, S.: Regions of interest extraction from color image based on visual saliency. Journal of Supercomp., doi:10.1007/s11227-010-0532-x.
Wang, Z., Liu, G., Yang, Y.: A New ROI Based Image Retrieval System using an auxiliary Gaussian Weighting Scheme. Multimedia Tools Application (2012), doi:10.1007/s11042-012-1059-3.
Yang, L., Geng, B., Cai, Y., Hua, X.-S.: Object Retrieval Using Visual Query Context. IEEE Transactions on Multimedia 13(6) (December 2011)
Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., Malik, J.: Blobworld:Image Segmentation Using Expectation-Maximization and Its Application to Image Querying. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)
Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Transactions On Pattern Analysis and Machine Intelligence 23(9) (September 2001)
Shrivastava, N., Tyagi, V.: Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching. Inform. Sci. 259, 212–224 (2013), http://dx.doi.org/10.1016/j.ins.2013.08.043
Liu, Y., Zhang, D., Lu, G.: Region-based image retrieval with high-level semantics using decision tree learning. Pattern Recognition 41, 2554–2570 (2008)
Jing, F., Li, M.: Relevance Feedback in Region-Based Image Retrieval. IEEE Transactions on Circuits and Systems for Video Technology 14(5) (May 2004)
Zhang, D., Islam, M.M., Lu, G., Hou, J.: Semantic Image Retrieval Using Region Based Inverted File. In: Proceedings of Digital Image Computing: Techniques and Applications, pp. 242–249 (2009)
Li, W.-J., Yeung, D.-Y.: Localized Content-Based Image Retrieval Through Evidence Region Identification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1666–1673 (2009)
Faloutsos, C., Barber, R., Flickner, M., Hafner, J., Niblack, W., Petkovic, D., Equitz, W.: Efficient and effective querying by image content, J. Intell. Inf. Syst. 3(3-4), 231–262 (1994)
Pentland, A., Picard, R.W., Scaroff, S.: Photobook: content-based manipulation for image databases. Int. J. Comput. Vision 18(3), 233–254 (1996)
Gupta, A., Jain, R.: Visual information retrieval, Commun. ACM 40(5), 70–79 (1997)
Smith, J.R., Chang, S.F.: Visualseek: a fully automatic content-based query system. In: Proceedings of ACM International Conference on Multimedia, pp. 87–98 (1996)
Ma, W.Y., Manjunath, B.: Netra: a toolbox for navigating large image databases. In: Proceedings of International Conference on Image Processing, pp. 568–571 (1997)
Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Mach. Intell. 23(9), 947–963 (2001)
Jing, F., Li, M., Zhang, L., Zhang, H., Zhang, B.: Learning in region-based image retrieval. In: Proceedings of International Conference on Image and Video Retrieval (CIVR 2003), pp. 206–215 (2003)
Town, C.P., Sinclair, D.: Content-based image retrieval using semantic visual categories, Society for Manufacturing Engineers, Technical Report MV01 211 (2001)
Cao, L., Fei-Fei, L.: Spatially coherent latent topic model for concurrent object segmentation and classification. In: Proceedings of IEEE International Conference in Computer Vision, ICCV (2007)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Proceedings of Computer Vision and Pattern Recognition, Workshop on Generative-Model Based Vision, pp. 178–185 (2004)
Chang, E., Tong, S.: SVM active—support vector machine active learning for image retrieval. In: Proceedings of ACM International Multimedia Conference, pp. 107–118 (October 2001)
Nguyen, G.P., Worring, M.: Relevance feedback based saliency adaptation in CBIR. ACM Multimedia Syst. 10(6), 499–512 (2005)
Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: An ontology approach to object-based image retrieval. In: Proceedings of International Conference on Image Processing, pp. 511–514 (2003)
Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. and Mach. Intel. (TPAMI) 28(7), 1088–1099 (2006)
Tao, D., Tang, X., Li, X., Rui, Y.: Kernel direct biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm. IEEE Trans. Multimedia (TMM) 8(4), 716–727 (2006)
Tao, D., Li, X., Maybank, S.J.: Negative samples analysis in relevance feedback, IEEE Trans. Knowl. IEEE Trans. Knowl. Data Eng. 19(4), 568–580 (2007)
Shrivastava, N., Tyagi, V.: An effective scheme for image texture classification based on binary local structure pattern. Visual Computer (2013), http://dx.doi.org/10.1007/s00371-013-0887-0
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Shrivastava, N., Tyagi, V. (2015). A Review of ROI Image Retrieval Techniques. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-12012-6_56
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12011-9
Online ISBN: 978-3-319-12012-6
eBook Packages: EngineeringEngineering (R0)