Object segmentation is considered as an important step in video analysis and has a wide range of practical applications. In this paper we propose a novel video segmentation method, based on a combination of watershed segmentation and mean-shift clustering. The proposed method segments video by clustering spatio-temporal data in a six-dimensional feature space, where the features are spatio-temporal coordinates and spectral attributes. The main novelty is an efficient data aggregation method employing watershed segmentation and local feature averaging. The experimental results show that the proposed algorithm significantly reduces the processing time by mean-shift algorithm and results in superior video segmentation where video objects are well defined and tracked throughout the time.

Date Published: 24 September 2007
PDF: 9 pages
Proc. SPIE 6696, Applications of Digital Image Processing XXX, 66962C (24 September 2007); doi: 10.1117/12.731738

Published in SPIE Proceedings Vol. 6696:
Applications of Digital Image Processing XXX
Andrew G. Tescher, Editor(s)