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Object-based image labeling through learning by example and multi-level segmentation
Date
2003-06-01
Author
Xu, Y
Duygulu, P
Saber, E
Tekalp, AM
Yarman Vural, Fatoş Tunay
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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We propose a method for automatic extraction and labeling of semantically meaningful image objects using "learning by example" and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate detail. Experiments have shown that the proposed multi-level image segmentation results in significant reduction in computation complexity for object extraction and labeling (compared to a single fine-level segmentation) by avoiding unnecessary tests of combinations in finer levels. The multi-level segmentation-based approach also achieves better accuracy in detection and labeling of small objects. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Subject Keywords
Signal Processing
,
Software
,
Artificial Intelligence
,
Computer Vision and Pattern Recognition
URI
https://hdl.handle.net/11511/62981
Journal
PATTERN RECOGNITION
DOI
https://doi.org/10.1016/s0031-3203(02)00250-9
Collections
Department of Computer Engineering, Article