Boosted MIML method for weakly-supervised image semantic segmentation
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| 003 | CHVBK | ||
| 005 | 20210128100128.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s11042-014-1967-5 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11042-014-1967-5 | ||
| 245 | 0 | 0 | |a Boosted MIML method for weakly-supervised image semantic segmentation |h [Elektronische Daten] |c [Yang Liu, Zechao Li, Jing Liu, Hanqing Lu] |
| 520 | 3 | |a Weakly-supervised image semantic segmentation aims to segment images into semantically consistent regions with only image-level labels are available, and is of great significance for fine-grained image analysis, retrieval and other possible applications. In this paper, we propose a Boosted Multi-Instance Multi-Label (BMIML) learning method to address this problem, the approach is built upon the following principles. We formulate the image semantic segmentation task as a MIML problem under the boosting framework, where the goal is to simultaneously split the superpixels obtained from over-segmented images into groups and train one classifier for each group. In the method, a loss function which uses the image-level labels as weakly-supervised constraints, is employed to suitable semantic labels to these classifiers. At the same time a contextual loss term is also combined to reduce the ambiguities existing in the training data. In each boosting round, we introduce an "objectness” measure to jointly reweigh the instances, in order to overcome the disturbance from highly frequent background superpixels. We demonstrate that BMIML outperforms the state-of-the-arts for weakly-supervised semantic segmentation on two widely used datasets, i.e., MSRC and LabelMe. | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a MIML |2 nationallicence | |
| 690 | 7 | |a Weakly-supervised |2 nationallicence | |
| 690 | 7 | |a Semantic segmentation |2 nationallicence | |
| 690 | 7 | |a Objectness |2 nationallicence | |
| 700 | 1 | |a Liu |D Yang |u National Laboratory of Pattern Recognition, Institution of Automation Chinese Academy of Sciences, Beijing, China |4 aut | |
| 700 | 1 | |a Li |D Zechao |u School of Computer Science, Nanjing University of Science and Technology, Nanjing, China |4 aut | |
| 700 | 1 | |a Liu |D Jing |u National Laboratory of Pattern Recognition, Institution of Automation Chinese Academy of Sciences, Beijing, China |4 aut | |
| 700 | 1 | |a Lu |D Hanqing |u National Laboratory of Pattern Recognition, Institution of Automation Chinese Academy of Sciences, Beijing, China |4 aut | |
| 773 | 0 | |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/2(2015-01-01), 543-559 |x 1380-7501 |q 74:2<543 |1 2015 |2 74 |o 11042 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11042-014-1967-5 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s11042-014-1967-5 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Liu |D Yang |u National Laboratory of Pattern Recognition, Institution of Automation Chinese Academy of Sciences, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Li |D Zechao |u School of Computer Science, Nanjing University of Science and Technology, Nanjing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Liu |D Jing |u National Laboratory of Pattern Recognition, Institution of Automation Chinese Academy of Sciences, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Lu |D Hanqing |u National Laboratory of Pattern Recognition, Institution of Automation Chinese Academy of Sciences, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/2(2015-01-01), 543-559 |x 1380-7501 |q 74:2<543 |1 2015 |2 74 |o 11042 | ||