Bayesian network based semantic image classification with attributed relational graph
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605447403 | ||
| 003 | CHVBK | ||
| 005 | 20210128100132.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150701xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s11042-014-1858-9 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11042-014-1858-9 | ||
| 245 | 0 | 0 | |a Bayesian network based semantic image classification with attributed relational graph |h [Elektronische Daten] |c [Chang-Yong Ri, Min Yao] |
| 520 | 3 | |a Semantic image classification is a hot issue of image mining. Information of spatial relations between objects in an image is one of the important semantic information of an image. However, the previous researches have not made full use of the spatial relations for image modeling and classification. In addition, to classify the images with Bayesian network, the accuracy of conditional probability estimation may be insufficient, because the learning methods of spatial contextual models have usually used a limited number of training samples. In this work, the semantic image modeling based on attributed relational graph has been proposed, in which the distance measure method between images was presented, therefore the object information and spatial relational information could be fully utilized. Then, the semantic distance between images based on attributed relational graph could be calculated for the support vector machine to obtain the joint conditional probability distribution of Bayesian network. Therefore the probabilistic estimation problem under the sparse training samples could be solved, and the accuracy of semantic image classification with Bayesian network was improved. Experimental results show the validity and reliability of this proposed method. | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a Semantic image classification |2 nationallicence | |
| 690 | 7 | |a Attributed relational graph |2 nationallicence | |
| 690 | 7 | |a Semantic distance |2 nationallicence | |
| 690 | 7 | |a Bayesian network |2 nationallicence | |
| 700 | 1 | |a Ri |D Chang-Yong |u School of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China |4 aut | |
| 700 | 1 | |a Yao |D Min |u School of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China |4 aut | |
| 773 | 0 | |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/13(2015-07-01), 4965-4986 |x 1380-7501 |q 74:13<4965 |1 2015 |2 74 |o 11042 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11042-014-1858-9 |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/s11042-014-1858-9 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ri |D Chang-Yong |u School of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Yao |D Min |u School of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/13(2015-07-01), 4965-4986 |x 1380-7501 |q 74:13<4965 |1 2015 |2 74 |o 11042 | ||