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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

The Method Proposal of Image Retrieval Based on K-Means Algorithm

verfasst von : Thanh The Van, Nguyen Van Thinh, Thanh Manh Le

Erschienen in: Trends and Advances in Information Systems and Technologies

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a content-based image retrieval system using the improved K-means algorithm with binary indexes of images. The created index, known as binary signatures of image, is based on image features including shape, location, and color. Firstly, we present the method of creating binary signature based on CIE-L*a*b* color space and Discrete Wavelet Frames. After that, the similarity measure between two binary signatures is presented. On the basis of k-means algorithm, we propose several improvements for clustering binary signatures used later to assess similarities between images. From that, the clustering algorithm for binary signatures of images is proposed. Next, we give the image retrieval algorithm based on the partitioned signature clusters. For illustrating our theoretical proposal, some experiments are conducted on common image sets including COREL, CBIRimages, and WANG.

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Vorheriges Kapitel Accuracy and Performance Analysis of Time Coherent 3D Animation Reconstruction from RGB-D Video
Nächstes Kapitel Performance Analysis of Vehicle Detection Techniques: A Concise Survey
Literatur
1.
2.
Alzu’bi, A., Amira, A., Ramzan, N.: Semantic content-based image retrieval - a comprehensive study. J. Vis. Commun. Image Represent. 32, 20–54 (2015)CrossRef
3.
An, Y., Baek, J., Shin, S., Chang, M., Park, J.: Classification of feature set using K-means clustering from histogram refinement method. In: Fourth International Networked Computing and Advanced Information Management (NCM 2008), Gyeongju, pp. 320–324. IEEE (2008)
4.
Banerjee, M., Bandyopadhyay, S., Pal, S.K.: A clustering approach to image retrieval using range based query and Mahalanobis distance. In: Skowron, A., Suraj, Z. (eds.) Rough Sets Intelligent System, pp. 79–91. Springer, Heidelberg (2013)CrossRef
5.
Chen, Y., Wang, J.Z., Krovetz, R.: CLUE: cluster-based retrieval of images by unsupervised learning. IEEE Trans. Image Process. 14(8), 1187–1201 (2005)CrossRef
6.
Chitkara, V., Nascimento, M.A., Mastaller, C.: Content-based image retrieval using binary signatures. Department of Computing Science, University of Alberta, Edmonton (2000)
7.
Chute, C.: Worldwide Digital Image 2015–2019 Forecast: The Image Capture and Share Bible, p. 13. Inter Data Corporation (2015)
8.
Deligiannidis, L., Arabnia, H.R.: Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition. Morgan Kaufmann, Burlington (2015). Elliot, S. (ed.) Elsevier, Waltham
9.
Deppisch, U.: S-tree - a dynamic balanced signature index for office retrieval. In: The 9th Annual International Conference on Research and Development in Information Retrieval, SIGIR, pp. 77–87. ACM, New York (1986)
10.
Hilbert, M.: A review of large-scale ‘How much information?’ inventories: variations, achievements and challenges. Inf. Res. 20(4), paper 688 (2015)
11.
12.
Lyman, P., Varian, H.R., Dunn, J., Strygin, A., Swearingen, K.: How much Information 2000. University of California, Berkeley (2000)
13.
Lin, C.-H., Chen, C.-C., Lee, H.-L., Liao, J.-R.: Fast K-means algorithm based on a level histogram for image retrieval. Expert Syst. Appl. 41(7), 3276–3283 (2014)CrossRef
14.
Muneesawang, P., Zhang, N., Guan, L.: Multimedia Database Retrieval - Technology and Applications. Edited by B. Furht, Springer, Cham (2014)CrossRef
15.
Nowakova, J., Prilepok, M., Snasel, V.: Medical image retrieval using vector quantization and fuzzy S-tree. J. Med. Syst. 41(18), 4–16 (2017)
16.
Nascimento, M.A., Chitkara, V.: Color-based image retrieval using binary signatures, pp. 687–692. ACM, Madrid (2002)
17.
Nascimento, M.A., Tousidou, E., Chitkara, V., Manolopoulos, Y.: Image indexing and retrieval using signature trees. Data Know. Eng. 43(1), 57–77 (2002)CrossRef
18.
Ren, G., Cai, J., Li, S., Yu, N., Tian, Q.: Scalable image search with reliable binary code. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 769–772. ACM, Orlando (2014)
19.
Saboorian, M.M., Jamzad, M., Rabiee, H.R.: User adaptive clustering for large image databases. In: International Conference on Pattern Recognition (ICPR), pp. 4271–4274. IEEE, Istanbul (2010)
20.
Van, T.T., Le, T.M.: Content-based image retrieval using a signature graph and a self-organizing map. Int. J. Appl. Math. Comput. Sci. 26(2), 423–438 (2016)MathSciNetCrossRef
21.
Unser, M.: Texture classication and segmentation using wavelet frames. IEEE Trans. Image Process. 4(11), 1549–1560 (1995)MathSciNetCrossRef
22.
Zakariya, S.M., Ali, R., Ahmad, N.: Combining visual features of an image at different precision value of unsupervised content based image retrieval. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4. IEEE, Coimbatore (2010)
Metadaten
Titel
The Method Proposal of Image Retrieval Based on K-Means Algorithm
verfasst von
Thanh The Van
Nguyen Van Thinh
Thanh Manh Le
Copyright-Jahr
2018
Buch
Trends and Advances in Information Systems and Technologies

Print ISBN: 978-3-319-77711-5

Electronic ISBN: 978-3-319-77712-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77712-2_45

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