|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM265169313 |
| 003 |
DE-627 |
| 005 |
20231224211707.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2017 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2016.2615423
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0883.xml
|
| 035 |
|
|
|a (DE-627)NLM265169313
|
| 035 |
|
|
|a (NLM)27723591
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Yanyun Qu
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Joint Hierarchical Category Structure Learning and Large-Scale Image Classification
|
| 264 |
|
1 |
|c 2017
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 30.07.2018
|
| 500 |
|
|
|a Date Revised 30.07.2018
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a We investigate the scalable image classification problem with a large number of categories. Hierarchical visual data structures are helpful for improving the efficiency and performance of large-scale multi-class classification. We propose a novel image classification method based on learning hierarchical inter-class structures. Specifically, we first design a fast algorithm to compute the similarity metric between categories, based on which a visual tree is constructed by hierarchical spectral clustering. Using the learned visual tree, a test sample label is efficiently predicted by searching for the best path over the entire tree. The proposed method is extensively evaluated on the ILSVRC2010 and Caltech 256 benchmark datasets. The experimental results show that our method obtains significantly better category hierarchies than other state-of-the-art visual tree-based methods and, therefore, much more accurate classification
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Li Lin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Fumin Shen
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chang Lu
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yang Wu
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yuan Xie
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Dacheng Tao
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 26(2017), 9 vom: 01. Sept., Seite 4331-4346
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:26
|g year:2017
|g number:9
|g day:01
|g month:09
|g pages:4331-4346
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2016.2615423
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 26
|j 2017
|e 9
|b 01
|c 09
|h 4331-4346
|