A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization

Hongwei Ge,Jing Dou,Zhiqiang Wang,Zhen Wang,Zehang Yan

doi:10.1155/2018/5987906

Hongwei Ge, Jing Dou + Show 3 more

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https://doi.org/10.1155/2018/5987906

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Abstract

Automatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints. Then, the multiview features are fused and dimensions are reduced based on multiview NMF algorithm. Finally, image annotation is achieved by using the new features through a KNN-based approach. Experiments validate that the proposed algorithm has achieved competitive performance in terms of accuracy and efficiency.

Highlights

The advent of Internet age brings the explosive growth of image resources
This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images
Automatic image annotation (AIA) refers to the process that computers automatically provide one or more semantic tags that can reflect the content of a specific image through algorithms

Summary

Introduction

The advent of Internet age brings the explosive growth of image resources. managing and retrieving images by semantic tags is a common and effective way, there are still a large number of untagged or not fully tagged images. Many multiview learning algorithms have been proposed for operating some tasks such as classification, retrieval, and clustering based on multiview features. Many image annotation algorithms use a variety of underlying features to improve annotation performance [8,9,10]. Many existing multiview learning algorithms are unsupervised; that is, they do not make use of the label information in the training set. Such fused features may not effectively contain the semantic relationship between samples. In GENMF, feature fusion and dimension reduction are firstly performed by the proposed graph embedded multiview NMF algorithm, and the new obtained features are used to annotate images through KNN-based approach

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The Proposed Methods

Experimental Studies

Experimental Results

Methods

Conclusions