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Image Segmentation and Labeling Using Free-Form Semantic Annotation

Tegen, Agnes; Weegar, Rebecka; Hammarlund, Linus; Oskarsson, Magnus LU ; Jiang, Fangyuan LU ; Medved, Dennis LU ; Nugues, Pierre LU and Åström, Karl LU (2014) 22nd International Conference on Pattern Recognition (ICPR 2014) In [Host publication title missing] p.2281-2286
Abstract
In this paper we investigate the problem of segmenting images using the information in text annotations. In contrast to the general image understanding problem, this type of annotation guided segmentation is less ill-posed in the sense that for the output there is higher consensus among human annotations. In the paper we present a system based on a combined visual and semantic pipeline. In the visual pipeline, a list of tentative figure-ground segmentations is first proposed. Each such segmentation is classified into a set of visual categories. In the natural language processing pipeline, the text is parsed and chunked into objects. Each chunk is then compared with the visual categories and the relative distance is computed using the... (More)
In this paper we investigate the problem of segmenting images using the information in text annotations. In contrast to the general image understanding problem, this type of annotation guided segmentation is less ill-posed in the sense that for the output there is higher consensus among human annotations. In the paper we present a system based on a combined visual and semantic pipeline. In the visual pipeline, a list of tentative figure-ground segmentations is first proposed. Each such segmentation is classified into a set of visual categories. In the natural language processing pipeline, the text is parsed and chunked into objects. Each chunk is then compared with the visual categories and the relative distance is computed using the word-net structure. The final choice of segments and their correspondence to the chunked objects are then obtained using combinatorial optimization. The output is compared to manually annotated ground-truth images. The results are promising and there are several interesting avenues for continued research. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
[Host publication title missing]
pages
2281 - 2286
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
22nd International Conference on Pattern Recognition (ICPR 2014)
external identifiers
  • wos:000359818002067
  • scopus:84919946714
ISSN
1051-4651
DOI
10.1109/ICPR.2014.396
language
English
LU publication?
yes
id
84497869-1395-4fe4-a472-79cbd9dd2bfe (old id 5052492)
date added to LUP
2015-02-23 13:37:01
date last changed
2017-01-01 06:16:17
@inproceedings{84497869-1395-4fe4-a472-79cbd9dd2bfe,
  abstract     = {In this paper we investigate the problem of segmenting images using the information in text annotations. In contrast to the general image understanding problem, this type of annotation guided segmentation is less ill-posed in the sense that for the output there is higher consensus among human annotations. In the paper we present a system based on a combined visual and semantic pipeline. In the visual pipeline, a list of tentative figure-ground segmentations is first proposed. Each such segmentation is classified into a set of visual categories. In the natural language processing pipeline, the text is parsed and chunked into objects. Each chunk is then compared with the visual categories and the relative distance is computed using the word-net structure. The final choice of segments and their correspondence to the chunked objects are then obtained using combinatorial optimization. The output is compared to manually annotated ground-truth images. The results are promising and there are several interesting avenues for continued research.},
  author       = {Tegen, Agnes and Weegar, Rebecka and Hammarlund, Linus and Oskarsson, Magnus and Jiang, Fangyuan and Medved, Dennis and Nugues, Pierre and Åström, Karl},
  booktitle    = {[Host publication title missing]},
  issn         = {1051-4651},
  language     = {eng},
  pages        = {2281--2286},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Image Segmentation and Labeling Using Free-Form Semantic Annotation},
  url          = {http://dx.doi.org/10.1109/ICPR.2014.396},
  year         = {2014},
}