Abstract
K-means clustering has been extremely popular in scene image classification. However, due to the random selection of initial cluster centers, the algorithm cannot always provide the most optimal results. In this paper, we develop a density-based k-means clustering. First, we calculate the density and distance for each feature vector. Then choose those features with high density and large distance as initial cluster centers. The remaining steps are the same with k-means. In order to evaluate our proposed algorithm, we have conducted several experiments on two-scene image datasets: Fifteen Scene Categories dataset and UIUC Sports Event dataset. The results show that our proposed method has good repeatability. Compared with the traditional k-means clustering, it can achieve higher classification accuracy when applied in multiclass scene image classification.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhou L (2012) Research on key technologies of scene classification and object recognition. Graduate School of National University of Defense Technology (in Chinese)
Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vision 42(3):145–175
Vogel J, Schiele B (2004) Natural scene retrieval based on a semantic modeling step. In: ACM international conference image video retrieval, New York, pp 207–215
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid classification for recognizing natural scene categories. In: IEEE computer society conference on computer vision and pattern recognition. New York, pp 2169–2178
Hartigan J, Wang M (1979) A k-means clustering algorithm. Appl Stat 28:100–108
Ester M, Kriegel HP, Sander J et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd vol 96, pp 226–231
Ankerst M, Breunig MM, Kriegel HP et al (1999) Optics: ordering points to identify the clustering structure ACM sigmod record. ACM 28(2):49–60
Hinneburg A, Keim DA (1998) An efficient approach to clustering in large multimedia databases with noise. In: KDD vol.98, pp 58–65
Redmond SJ, Heneghan C (2007) A method for initialising the k-means clustering algorithm using Kd-trees. Pattern Recogn Lett 28(8):965–973
Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Inter J Comput Vis 2(60):91–110
Chang CC, LIN CJ (2001) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm
Barla A, Odone F, Verri A (2003) Histogram intersection kernel for image classification. In: International Conference on Image Processing vol.3(2):p III-513–16
Li LJ, Fei-Fei L (2007) What, where and who? Classifying events by scene and object recognition. In: IEEE 11th international conference on IEEE. computer vision ICCV, pp 1–8
Acknowledgments
This project is partly supported by NSF of China (61375001), partly supported by the open project program of Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University (No.CDLS-2014-04), partly supported by China Postdoctoral Science Foundation (2013M540404) and partly supported by the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120092110024).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xie, K., Wu, J., Yang, W., Sun, C. (2015). K-Means Clustering Based on Density for Scene Image Classification. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-662-46469-4_40
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46468-7
Online ISBN: 978-3-662-46469-4
eBook Packages: EngineeringEngineering (R0)