.png){kind=icon}
Return
Multiple feature clustering for image sequence segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/S0167-8655(01)00053-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a segmentation scheme in order to develop a method which can identify homogeneous regions to represent higher level objects for content-based functionality. The proposed scheme extracts multiple features, such as motion and texture, on the pixel basis. Different weights are applied to each feature components based on motion confidence measures. The proposed scheme consists of two phases. In the first phase, a multiple feature space is transformed to one-dimensional label space using a self-organizing feature maps (SOFM) neural network clustering method. In the second phase, the neural network outputs are merged in order to generate desired segmentation resolution. Our experimental results and performance analysis show the validity of the proposed scheme. (C) 2001 Published by Elsevier Science B.V.
Keywords:
image segmentation
motion estimation
semantic object
MPEG-4
SOFM
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
8.0K
Citations:
1.6W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSave
no more
no more