Adaptive Tag Selection for Image Annotation
Xixi He, Xirong Li, Gang Yang, Jieping Xu, Qin Jin

TL;DR
This paper introduces an adaptive tag selection method for image annotation that dynamically determines the number of relevant tags per image, outperforming fixed top-k approaches and applicable as a plug-in to existing systems.
Contribution
The paper proposes a novel adaptive tag selection approach that divides tags into seen and novel sets, estimating the number of tags to select without ground truth for all tags.
Findings
Achieved an F-score of 0.223 on ImageCLEF 2014, surpassing the 0.122 of top-k strategy.
Method effectively estimates relevant tags without full ground truth data.
Can be integrated into existing image annotation systems as a plug-in.
Abstract
Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Text and Document Classification Technologies
