Abstract
In this paper, we introduce a technique to detect a target object quickly. Our idea is based on onservation on the clusters into which an image is divided by hierarchical k-means clustering with space feature and color feature. This clustering method has the advantage of extracting the region of an object with some varied size. We insist that our idea should lead to detect a target object quickly, because it is not necessary to search the locations containing no targets. First, we evaluate our clustering method and second, we demonstrate that our method is effective on an object detection by applying to our face detection system. We show that the detection time can be reduced by 24%.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hjelmas, E., Low, B.: Face detection:a survey. Comp. Vis. Img. Und. 83, 236–274 (2001)
Yang, M., Kriegman, D., Ahuja, N.: Detecting faces in images:a survey. IEEE Trans. Patt. Anal. Mach. Intell. 24, 34–58 (2002)
Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Patt. Anal. Mach. Intell. 20, 23–38 (1998)
Pashler, H. (ed.): Attention. Psychology Press (2001)
Wang, C., Brandstein, M.: Multi-source face tracking with audio and visual data. In: Proc. IEEE 3rd Workshop on Multimedia Signal Processing, pp. 169–174 (1999)
Jacquin, A., Eleftheriadis, A.: Automatic location tracking of faces and facial features in video sequences. In: Proc. 1st Intl. Workshop on Automatic Face and Gesture Recognition, pp. 142–147 (1995)
Ikeda, H., Kato, N., Kashimura, H., Shimizu, M.: Scale, rotation, and translation invariant fast face detection system. In: Proc. of the 5th IASTED Intl. Conf. on Signal and Image Processing, pp. 146–151 (2003)
Kato, N., Ikeda, H., Kashimura, H., Shimizu, M.: Scaling, rotation, and translation invariant image recognition using competing multiple subspaces. In: Proc. INNS-IEEE Intl. Joint Conf. on Neural Networks, pp. 1268–1273 (2003)
Su, M., Chou, C.: A modified verison of the k-means algorithm with a distance based on cluster symmetry. IEEE Trans. Patt. Anal. Mach. Intell. 6, 674–680 (2001)
Matousek, J.: On approximate geometric k-clustering. Discrete & Comp. Geometry 24, 61–84 (2000)
Tailor, J., Gisler, G.: A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation. In: Proc. SPIE, vol. 3159 pp. 108–118 (1997)
Murtagh, F.: A survey of recent advances in hierarchical clustering algorithms. The Comp. J. 26, 354–359 (1983)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fukui, M., Kato, N., Ikeda, H., Kashimura, H. (2004). Size-Independent Image Segmentation by Hierarchical Clustering and Its Application for Face Detection. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_105
Download citation
DOI: https://doi.org/10.1007/978-3-540-30499-9_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
eBook Packages: Springer Book Archive