×

Multi region based image retrieval system. (English) Zbl 1322.68067

Summary: Multimedia information retrieval systems continue to be an active research area in the world of huge and voluminous data. The paramount challenge is to translate or convert a visual query from a human and find similar images or videos in large digital collection. In this paper, a technique of region based image retrieval, a branch of Content Based Image Retrieval, is proposed. The proposed model does not need prior knowledge or full semantic understanding of image content. It identifies significant regions in an image based on feature-based attention model which mimic viewer’s attention. The Curvelet Transform in combination with colour descriptors are used to represent each significant region in an image. Experimental results are analysed and compared with the state-of-the-art Region Based Image Retrieval Technique.

MSC:

68P20 Information storage and retrieval of data
68T10 Pattern recognition, speech recognition
68U10 Computing methodologies for image processing
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
PDF BibTeX XML Cite
Full Text: DOI Link

References:

[1] Aroussi E, Ghouzali M, Hassouni S E, Rziza M and Aboutajdine M 2009 Curvelet-based feature extraction with B-LDA for face recognition. Proc. Int. Conf. Computer Systems and Appl. (AICCSA), 444–448
[2] Candès E J, Demanet L, Donoho D L and Ying L 2006 Fast discrete curvelet transforms. SIAM J. Multiscale Model. Simul. 5(3): 861–899 · Zbl 1122.65134
[3] Carson C, Belongie S, Greenspan H and Malik J 2002 Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 8(8): 1026–1038 · Zbl 05111981
[4] Djordjevic D and Izquierdo E 2007 An object- and user-driven system for semantic-based image annotation and retrieval. IEEE Trans. Circuits and Syst. Video Technol. 17(3): 313–323 · Zbl 05451698
[5] Feng H and Chua T S 2003 A boostrapping approach to annotating large image collection. Proc. Workshop Multimedia Information Retrieval in ACM Multimedia, 55–62
[6] Frintrop S, Rome E and Christensen H I 2010 Computational visual attention systems and their cognitive foundations: A Survey. ACM Trans. Appl. Percept. 7(6): 1–39
[7] Hoffman D D and Singh M 1997 Salience of visual parts. Cognition 63: 29–78
[8] Ilonen J and Kämäräinen J 2006 Simplegabor - Multiresolution Gabor Feature Toolbox, Gabor Feature Toolbox version-1.0.0 http://www2.it.lut.fi/project/simplegabor/downloads/src/simplegabortb/
[9] Islam M M, Zhang D and Lu G 2009a Rotation invariant Curvelet feature for texture image retrieval. Proc. IEEE Int. Conf. Multimedia and Expo, 562–565
[10] Islam M M, Zhang D and Lu G 2009b Region based color image retrieval using Curvelet transform. Proc. ACCV 2: 448–457
[11] Konstantinidis K and Andreadis I 2005 Performance and computational burden of histogram based color image retrieval techniques. J. Comp. Methods in Sci. and Eng 4: 141–147 · Zbl 1198.68216
[12] Liu Y, Zhang D, Lu G and Ma W Y 2007 A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40: 262–282 · Zbl 1103.68503
[13] Manipoonchelvi P and Muneeswaran K 2011 Significant region based image retrieval using curvelet transform. Int. Conf. Recent Advancements in Electrical, Electronics, and Control Eng. (ICONRAEeCE), 291–294
[14] Muneeswaran K, Ganesan L, Arumugam S and Soundar K R 2006 Texture image segmentation using combined features from spatial and spectral distribution. Pattern Recognition Lett. 27(7): 755–764
[15] Rubner Y, Tomasi C and Guibas L J 2000 The Earth Mover’s Distance as a Metric for Image Retrieval. Int. J. Comput. Vision 40(2): 99–121 · Zbl 1012.68705
[16] Sumana I, Islam M, Zhang D S and Lu G 2008 Content Based Image Retrieval using Curvelet Transform. Proc. IEEE Int. workshop Multimedia Signal processing, Australia, 11–16
[17] Sun Y and Ozawa S 2005 A hierarchical approach for region-based image retrieval. Proc. IEEE Int. Conf. Systems, Man and Cybernetics, 1117–1124
[18] Swain M J and Ballard D H 1991 Color Indexing. Int. J. Comput. Vision 7(1): 11–32
[19] Wang J Z, Li J and Wiederhold G 2001 SIMPLIcity: Semantics-sensitive Integrated Matching for Picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9): 947–963 · Zbl 05112193
[20] Wang W, Song Y and Zhang A 2002 Semantics-Based Image Retrieval by Region Saliency. Image and Video Retrieval, Lecture Notes in Comput. Sci. 2383: 29–37 · Zbl 1015.68781
[21] Wolfe J M, Horowitz T S, Kenner N, Hyle M and Vasan N 2004 How fast can you change your mind? The speed of top-down guidance in visual search. Vision Res. 44: 1411–1426
[22] Zhang Y J 2007 Semantic-Based Visual Information Retrieval, IRM press
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.