Image auto-annotation via tag-dependent random search over range-constrained visual neighbours
| LEADER | caa a22 4500 | ||
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| 001 | 605447497 | ||
| 003 | CHVBK | ||
| 005 | 20210128100132.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150601xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s11042-013-1811-3 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11042-013-1811-3 | ||
| 245 | 0 | 0 | |a Image auto-annotation via tag-dependent random search over range-constrained visual neighbours |h [Elektronische Daten] |c [Zijia Lin, Guiguang Ding, Mingqing Hu] |
| 520 | 3 | |a The quantity setting of visual neighbours can be critical for the performance of many previously proposed visual-neighbour-based (VNB) image auto-annotation methods. And in those methods, each candidate tag of a to-be-annotated image would be better to have its own trustworthy part of visual neighbours for score prediction. Hence in this paper we propose to use a constrained range rather than an identical and fixed number of visual neighbours for VNB methods to allow more flexible choices of neighbours, and then put forward a novel tag-dependent random search process to estimate the tag-dependent trust degrees of visual neighbours for each candidate tag. We further propose an effective image auto-annotation method termed TagSearcher based on a widely-used conditional probability model for auto-annotation, considering image-dependent weights of visual neighbours, tag-dependent trust degrees of visual neighbours and votes for a candidate tag from visual neighbours. Extensive experiments conducted on both a benchmark dataset and real-world web images present that the proposed TagSearcher can yield inspiring annotation performance and also reduce the performance sensitivity to the quantity setting of visual neighbours. | |
| 540 | |a Springer Science+Business Media New York, 2013 | ||
| 690 | 7 | |a Image auto-annotation |2 nationallicence | |
| 690 | 7 | |a TagSearcher |2 nationallicence | |
| 690 | 7 | |a Tag-dependent random search |2 nationallicence | |
| 690 | 7 | |a Range-constrained visual neighbours |2 nationallicence | |
| 700 | 1 | |a Lin |D Zijia |u Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China |4 aut | |
| 700 | 1 | |a Ding |D Guiguang |u School of Software, Tsinghua University, 100084, Beijing, China |4 aut | |
| 700 | 1 | |a Hu |D Mingqing |u Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China |4 aut | |
| 773 | 0 | |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/11(2015-06-01), 4091-4116 |x 1380-7501 |q 74:11<4091 |1 2015 |2 74 |o 11042 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11042-013-1811-3 |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/s11042-013-1811-3 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Lin |D Zijia |u Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ding |D Guiguang |u School of Software, Tsinghua University, 100084, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Hu |D Mingqing |u Institute of Computing Technology, Chinese Academy of Sciences, 100190, 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 74/11(2015-06-01), 4091-4116 |x 1380-7501 |q 74:11<4091 |1 2015 |2 74 |o 11042 | ||