Deep neural network based image annotation

doi:10.1016/j.patrec.2015.07.037

2015-11-01

PRE

DOI：10.1016/j.patrec.2015.07.037

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摘要

En 中文

Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two stage learning scheme that consists of (i) learning to fine-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on a variety of public datasets evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms. (C) 2015 Elsevier B.V. All rights reserved.

Keyword:

Deep learning

Multi-label

Multi-modal

Image annotation