Abstract
Similarity metrics plays an important role in Content-Based Image Retrieval (CBIR). This article introduces a new technique called Descriptive Proximal Coverage (DPC) to measure the quantifiable similarity index between images. This work is based on Near Neighbourhood approach in which perceptually relevant information is extracted from group of objects based on their description. Two images are considered as sets of perceptual objects and affinities between objects is defined by a tolerance relation. Two visual objects are similar if the difference between their descriptions is smaller than a tolerable level of error. Existing Notion of nearness stems from the observation that in nature it is rare to find exact objects but similar objects are often seen. It is imperative to provide a numeric value which will quantify similarity and nearness between images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
R. Datta, D. Joshi, J. Li, J. Z. Wang, Image retrieval: Ideas, influences, and trends of the new age, ACM Computing Survey, 40(2), 2008, pp. 1–60, 1348248.
N. Jhanwar, S. Chaudhuri, G. Seetharaman, B. Zavidovique, Content based image retrieval using motif cooccurrence matrix, Image and Vision Computing, 22(14), 2004, pp. 1211–1220.
A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain, Content-based image retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 2000, pp. 1349–1380.
D. Huttenlocher, G. Klanderman, W. Rucklidge, Comparing Images Using the Hausdorff Distance, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 1993, pp. 850–863.
M. Shapiro, M. Blaschko, Stability of Hausdorff-based Distance Measures, Proceedings of IASTED Visualization, Imaging, and Image Processing, Marbella, Spain, 2004.
C.H. Lin, R.T. Chen, Y.K. Chan, “A smart content-based image retrieval system based on color and texture feature, Image and Vision Computing”, 27(6), 2009, pp. 658–665.
G.H. Liu, L. Zhang, Y.K. Hou, Z.Y. Li, J.Y. Yang, Image retrieval based on multi-texton histogram, Pattern Recognition, 43(7), 2010, pp. 2380–2389,.
C. P. Lee, Robust image segmentation using active contours: Level set approaches, Ph.D. dissertation, North Carolina State University, 2005.
M. A. Stricker, M. Orengo, Similarity of color images, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2420, 1995, pp. 381–392.
T. Gevers, A.W.M Smeulders, Image Search Engines, An Overview, The International Society for Optical Engineering, 2003.
K. Yang, J. Trewn, Multivariate Statistical Methods in Quality Management, McGraw-Hill Professional, 1st Edition 2004.
P. N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley, 2005.
C. Spearman, The proof and measurement of association between two things, The American Journal of Psychology, 15(72–101), 1904, pp.72–101.
J.F Peters, General theory about nearness of objects, Applied Mathematical Sciences 1(53), 2007, pp. 2609–2629.
J.F Peters, Special theory about nearness of objects, Fundamenta Informaticae, 75(1–4), 2007, pp.407–433.
C.J Henry, S. Ramanna, Maximal clique enumeration in finding near neighbourhoods, Transactions on Rough Sets XVI, Springer Berlin Heidelberg, 2013, pp. 103–124.
C. Henry, J. F. Peters, Image pattern recognition using approximation spaces and near sets, Proceedings of the Eleventh International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computer, Joint Rough Set Symposium (JRS07), Lecture Notes in Artificial Intelligence, 4482, 2007.
C. Henry, J. F. Peters, Perception based image classification, Computational Intelligence Laboratory, University of Manitoba, UMCI Laboratory Technical Report No. TR-2009-016, 2009.
J. F. Peters, S. Ramana, Affinities between perceptual granules: Foundations and perspectives, Human-Centric Information Processing Through Granular Modelling, Springer-Verlag, 2008, pp. 409–436.
J.F. Peters, P. Wasilewski, Tolerance spaces: Origins, theoretical aspects and applications. Information Sciences 195, 2012, pp. 211–225.
A.B. Sossinsky, Tolerance space theory and some applications, Acta Applicandae Mathematicae: An International Survey Journal on Applying Mathematics and Mathematical Applications 5(2), 1986, pp. 137–167.
E.C Zeeman, The topology of the brain and the visual perception, Topoloy of 3-manifolds and selected topics, Prentice Hall, New Jersey, 1965, pp. 240–256.
C. Henry, G. Smith, Proximity Systems, UM CI Labs Technical Report Number TR-2012-021, University of Manitoba, Canada, 2014.
V. Jain, A. Mukherjee, The Indian Face Database, http://viswww.cs.umass.edu/~vidit/IndianFaceDatabase.
J. Z. Wang, J. Li, and G. Wiederhold, Simplicity: Semantics-sensitive integrated matching for picture libraries, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 2001, pp. 947–963, Database URL:http://wang.ist.psu.edu/docs/related/.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer India
About this paper
Cite this paper
Sajwan, M., Patnaik, K.S. (2017). Quantifiable Image Nearness Approach Using Descriptive Neighbourhood. In: Lobiyal, D., Mohapatra, D., Nagar, A., Sahoo, M. (eds) Proceedings of the International Conference on Signal, Networks, Computing, and Systems. Lecture Notes in Electrical Engineering, vol 395. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3592-7_8
Download citation
DOI: https://doi.org/10.1007/978-81-322-3592-7_8
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-3590-3
Online ISBN: 978-81-322-3592-7
eBook Packages: EngineeringEngineering (R0)