Markov random field based fusion for supervised and semi-supervised multi-modal image classification
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| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s11042-014-2018-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11042-014-2018-y | ||
| 245 | 0 | 0 | |a Markov random field based fusion for supervised and semi-supervised multi-modal image classification |h [Elektronische Daten] |c [Liang Xie, Peng Pan, Yansheng Lu] |
| 520 | 3 | |a In recent years, there has been a massive explosion of multimedia content on the web, multi-modal examples such as images associated with tags can be easily accessed from social website such as Flickr. In this paper, we consider two classification tasks: supervised and semi-supervised multi-modal image classification, to take advantage of the increasing multi-modal examples on the web. We first propose a Markov random field (MRF) based fusion method: discriminative probabilistic graphical fusion (DPGF) for the supervised multi-modal image classification, which can make use of the associated tags to enhance the classification performance. Based on DPGF, we then propose a three-step learning procedure: DPGF+RLS+SVM, for the semi-supervised multi-modal image classification, which uses both the labeled and unlabeled examples for training. Experimental results on two datasets: PASCAL VOC'07 and MIR Flickr, show that our methods can well exploit the multi-modal data and unlabeled examples, and they also outperform previous state-of-the-art methods in both two multi-modal image classification. Finally we consider the weakly supervised condition where class labels are from image tags which are noisy. Our semi-supervised approach also improves the classification performance in this case. | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a Multi-modal classification |2 nationallicence | |
| 690 | 7 | |a Image classification |2 nationallicence | |
| 690 | 7 | |a Semi-supervised learning |2 nationallicence | |
| 690 | 7 | |a Markov random field |2 nationallicence | |
| 700 | 1 | |a Xie |D Liang |u School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China |4 aut | |
| 700 | 1 | |a Pan |D Peng |u School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China |4 aut | |
| 700 | 1 | |a Lu |D Yansheng |u School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China |4 aut | |
| 773 | 0 | |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/2(2015-01-01), 613-634 |x 1380-7501 |q 74:2<613 |1 2015 |2 74 |o 11042 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11042-014-2018-y |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/s11042-014-2018-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Xie |D Liang |u School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Pan |D Peng |u School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Lu |D Yansheng |u School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, 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), 613-634 |x 1380-7501 |q 74:2<613 |1 2015 |2 74 |o 11042 | ||