Size-independent image segmentation by hierarchical clustering and its application for face detection

1Citations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

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 delection system. We show that the detection time can be reduced by 24%. © Springer-Verlag Berlin Heidelberg 2004.

Cite

CITATION STYLE

APA

Fukui, M., Kato, N., Ikeda, H., & Kashimura, H. (2004). Size-independent image segmentation by hierarchical clustering and its application for face detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3316, 686–693. https://doi.org/10.1007/978-3-540-30499-9_105

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free