Article

An adaptive graph model for automatic image annotation

Authors:

Hanqing LuAuthors Info & Claims

MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrieval

Pages 61 - 70

https://doi.org/10.1145/1178677.1178689

Published: 26 October 2006 Publication History

Abstract

Automatic keyword annotation is a promising solution to enable more effective image search by using keywords. In this paper, we propose a novel automatic image annotation method based on manifold ranking learning, in which the visual and textual information are well integrated. Due to complex and unbalanced data distribution and limited prior information in practice, we design two new schemes to make manifold ranking efficient for image annotation. Firstly, we design a new scheme named the Nearest Spanning Chain (NSC) to generate an adaptive similarity graph, which is robust across data distribution and easy to implement. Secondly, the word-to-word correlations obtained from WordNet and the pairwise co-occurrence are taken into consideration to expand the annotations and prune irrelevant annotations for each image. Experiments conducted on standard Corel dataset and web image dataset demonstrate the effectiveness and efficiency of the proposed method for image annotation.

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Cited By

Wang LDing ZFu Y(2021)Generic Multi-label Annotation via Adaptive Graph and Marginalized AugmentationACM Transactions on Knowledge Discovery from Data10.1145/345188416:1(1-20)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3451884
Wang LLiu YDi HQin CSun GFu Y(2021)Semi-Supervised Dual Relation Learning for Multi-Label ClassificationIEEE Transactions on Image Processing10.1109/TIP.2021.312200330(9125-9135)Online publication date: 2021
https://doi.org/10.1109/TIP.2021.3122003
Tang CLiu XWang PZhang CLi MWang L(2019)Adaptive Hypergraph Embedded Semi-Supervised Multi-Label Image AnnotationIEEE Transactions on Multimedia10.1109/TMM.2019.290986021:11(2837-2849)Online publication date: Nov-2019
https://doi.org/10.1109/TMM.2019.2909860
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Index Terms

An adaptive graph model for automatic image annotation
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrieval

October 2006

344 pages

ISBN:1595934952

DOI:10.1145/1178677

General Chairs:
James Z. Wang
The Pennsylvania State University
,
Nozha Boujemaa
INRIA Rocquencourt, France
,
Yixin Chen
The University of Mississippi

Copyright © 2006 ACM.

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Publication History

Published: 26 October 2006

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MM06: The 14th ACM International Conference on Multimedia 2006

October 26 - 27, 2006

California, Santa Barbara, USA

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Cited By

Wang LDing ZFu Y(2021)Generic Multi-label Annotation via Adaptive Graph and Marginalized AugmentationACM Transactions on Knowledge Discovery from Data10.1145/345188416:1(1-20)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3451884
Wang LLiu YDi HQin CSun GFu Y(2021)Semi-Supervised Dual Relation Learning for Multi-Label ClassificationIEEE Transactions on Image Processing10.1109/TIP.2021.312200330(9125-9135)Online publication date: 2021
https://doi.org/10.1109/TIP.2021.3122003
Tang CLiu XWang PZhang CLi MWang L(2019)Adaptive Hypergraph Embedded Semi-Supervised Multi-Label Image AnnotationIEEE Transactions on Multimedia10.1109/TMM.2019.290986021:11(2837-2849)Online publication date: Nov-2019
https://doi.org/10.1109/TMM.2019.2909860
Wang LDing ZFu Y(2018)Adaptive graph guided embedding for multi-label annotationProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304889.3305049(2798-2804)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304889.3305049
Tian FShen XLiu X(2018)Multimedia automatic annotation by mining label set correlationMultimedia Tools and Applications10.1007/s11042-017-5170-377:3(3473-3491)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11042-017-5170-3
Nair LManjusha RParameswaran L(2018)Kernel Based Approaches for Context Based Image AnnotatıonComputational Vision and Bio Inspired Computing10.1007/978-3-319-71767-8_21(249-258)Online publication date: 20-Feb-2018
https://doi.org/10.1007/978-3-319-71767-8_21
Pobar MIvasic-Kos M(2016)Automatic image annotation refinement2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)10.1109/MIPRO.2016.7522345(1324-1329)Online publication date: May-2016
https://doi.org/10.1109/MIPRO.2016.7522345
Zhiqiang ZHua SYun WZou Q(2016)Annotation-retrieval reinforcement by visual cognition modeling on manifoldNeurocomputing10.1016/j.neucom.2015.07.162215:C(150-159)Online publication date: 26-Nov-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.07.162
Ke XGuo W(2016)Multi-scale salient region and relevant visual keywords based model for automatic image annotationMultimedia Tools and Applications10.1007/s11042-014-2318-275:20(12477-12498)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s11042-014-2318-2
Su FXue LHauptmann ANgo CXue XJiang YSnoek CVasconcelos N(2015)Graph Learning on K Nearest Neighbours for Automatic Image AnnotationProceedings of the 5th ACM on International Conference on Multimedia Retrieval10.1145/2671188.2749383(403-410)Online publication date: 22-Jun-2015
https://dl.acm.org/doi/10.1145/2671188.2749383
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