A neuro-fuzzy approach for segmentation of human objects in image sequences

We propose a novel approach for segmentation of human objects, including face and body, in image sequences. Object segmentation is important for achieving a high compression ratio in modern video coding techniques, e.g., MPEG-4 and MPEG-7, and human objects are usually the main parts in the video st...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2003-06, Vol.33 (3), p.420-437
Main Authors: Lee, Shie-Jue, Ouyang, Chen-Sen, Du, Shih-Huai
Format: Article
Language:English
Subjects:
Algorithms
Face detection
Frames
Fuzzy
Fuzzy logic
Fuzzy neural networks
Fuzzy set theory
Human
Humans
Image coding
Image segmentation
Image sequences
MPEG 4 Standard
Object segmentation
Segmentation
Streaming media
Streams
Studies
Video coding
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Summary:We propose a novel approach for segmentation of human objects, including face and body, in image sequences. Object segmentation is important for achieving a high compression ratio in modern video coding techniques, e.g., MPEG-4 and MPEG-7, and human objects are usually the main parts in the video streams of multimedia applications. Existing segmentation methods apply simple criteria to detect human objects, leading to the restriction of the usage or a high segmentation error. We combine temporal and spatial information and employ a neuro-fuzzy mechanism to overcome these difficulties. A fuzzy self-clustering technique is used to divide the base frame of a video stream into a set of segments which are then categorized as foreground or background based on a combination of multiple criteria. Then, human objects in the base frame and the remaining frames of the video stream are precisely located by a fuzzy neural network constructed with the fuzzy rules previously obtained and is trained by a singular value decomposition (SVD)-based hybrid learning algorithm. The proposed approach has been tested on several different video streams, and the results have shown that the approach can produce a much better segmentation than other methods.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2003.811765