Image auto-annotation via tag-dependent random search over range-constrained visual neighbours

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 neighbo...

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Bibliographic Details
Published in:Multimedia tools and applications 2015-06, Vol.74 (11), p.4091-4116
Main Authors: Lin, Zijia, Ding, Guiguang, Hu, Mingqing
Format: Article
Language:English
Subjects:
Analysis
Annotations
Benchmarking
Computer Communication Networks
Computer Science
Conditional probability
Data Structures and Information Theory
Estimates
Image processing systems
Image retrieval
Multimedia
Multimedia Information Systems
Probability
Search process
Searching
Semantics
Social networks
Special Purpose and Application-Based Systems
Studies
Trust
Visual
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Summary: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.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-013-1811-3