Abstract
Most existing methods for content-based image retrieval handle an image as a whole, instead of focusing on an object of interest. This paper proposes object-based image retrieval based on the dominant color pairs between adjacent regions. From a segmented image, the dominant color pairs between adjacent regions are extracted to produce color adjacency matrix, from which candidate regions of DB images are selected. The similarity measure between the query image and candidate regions in DB images is computed based on the color correlogram technique. Experimental results show the performance improvement of the proposed method over existing methods.
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
Pentland, A., Picard, R.W., Sclaroff, S., et al.: Photobook: Tools for Content-based Manipulation of Image Database. In: SPIE, Proc. In Storage and Retrieval for Image and Video Databases II, vol. 2185 (Febrauary 1994)
Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E., Petkovic, D., Yanker, P.: The QBIC Project: Querying Images by Content Using Color, Texture, and Shape. In: SPIE, vol. 1908, pp. 173–187 (1993)
Ma, W.Y., Manjunath, B.S.: Netra: A Toolbox for Navigationg Large Image Database. In: IEEE International Conference on Image Processing (1997)
Smith, J.R., Chang, S.F.: VisualSEEk: A Fully Automated Content-based Image Query System. In: Proc. ACM Multimedia, Boston, MA (1996)
Oh, J.B., Moon, Y.S.: Content-based Image Retrieval Based on Scale-Space Theory. IEICE Trans. Fundamental (June 1999)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image Indexing Using Color Correlograms. In: Proc. of the IEEE conference on Computer Vision and Pattern Recognition, pp. 762–768 (1997)
Swain, M., Ballard, D.: Color Indexing. International Journal of Computer Vision 7(1), 11–32 (1991)
Das, M., Riseman, E.M., Draper, B.: FOCUS: Searching for Muti-colored Objects in a Diverse Image Database. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762–768 (1997)
Wang, D.: Unsupervised Video Segmen-tation based on Watersheds and Temporal Tracking. IEEE Trans. on Circuits and System for Video Technology 8(5), 539–546 (1998)
Manjunath, B.S., Ohm, J.R., Vasudevan, V., Yamada, A.: Color and Texture Descriptors. IEEE Trans. Circuits and Systems for Video Tech. 11(6) (June 2001)
Lee, H.Y., Lee, H.K., Ha, Y.H.: Spatial Color Descriptor for Image Retrieval and Video Segmentation. IEEE Trans. on Multimedia 5(3), 358–367 (2003)
Androutsos, D., Plataniotis, K.N., Venetsanopoulos, A.N.: A Vector Angular Distance Measure for Indexing and Retrieval of Color. In: Proc. Storage & Retrieval for Image and Video Databases VII, SPIE-3656, San Jose, USA, pp. 604–613 (1999)
Androutsos, D., Plataniotis, K.N., Venetsanopoulo, A.N.: A Novel Vector-Based Approach to Color Image Retrieval Using a Vector Angular-Based Distance Measure. Computer Vision and Image Understanding 75, 46–58 (1999)
ISO/IEC JTC1/SC29/WG1/ Core Experiment on MPEG-7 Color and Texture Descriptors, Doc. N2819, MPEG Vancouber Meeting (July 1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, K.T., Moon, Y.S. (2006). Object-Based Image Retrieval Using Dominant Color Pairs Between Adjacent Regions. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751649_44
Download citation
DOI: https://doi.org/10.1007/11751649_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34079-9
Online ISBN: 978-3-540-34080-5
eBook Packages: Computer ScienceComputer Science (R0)