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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2018

11.01.2018

Training Visual-Semantic Embedding Network for Boosting Automatic Image Annotation

verfasst von: Weifeng Zhang, Hua Hu, Haiyang Hu

Erschienen in: Neural Processing Letters | Ausgabe 3/2018

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Abstract

Image auto-annotation which annotates images according to their semantic contents has become a research focus in computer vision, as it helps people to edit, retrieve and understand large image collections. In the last decades, researchers have proposed many approaches to solve this task and achieved remarkable performance on several standard image datasets. In this paper, we train neural networks using visual and semantic ranking loss to learn visual-semantic embedding. This embedding can be easily applied to nearest-neighbor based models to boost their performance on image auto-annotation. We test our method on four challenging image datasets, reporting comparison results with existing works. Experimental results show that our method can be applied to several state-of-the-art nearest-neighbor based models including TagProp and 2PKNN, and significantly improves their performance.
Vorheriger Artikel Stability and Hopf Bifurcation of a Three-Neuron Network with Multiple Discrete and Distributed Delays
Nächster Artikel Robust Mittag-Leffler Synchronization for Uncertain Fractional-Order Discontinuous Neural Networks via Non-fragile Control Strategy

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Fußnoten
1
We also tried to use ReLU which perform slightly inferior than using SER (F1 values decrease 3–5% in our experiments on four datasets).
 
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Metadaten
Titel
Training Visual-Semantic Embedding Network for Boosting Automatic Image Annotation
verfasst von
Weifeng Zhang
Hua Hu
Haiyang Hu
Publikationsdatum
11.01.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9753-9

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