NDLI: Improving Automatic Image Annotation with Google Semantic Link

Content Provider	IEEE Xplore Digital Library
Author	Haijiao Xu Peng Pan Yansheng Lu Chunyan Xu Deng Chen
Copyright Year	2014
Description	Author affiliation: Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Chunyan Xu) \|\| Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China (Haijiao Xu; Peng Pan; Yansheng Lu; Deng Chen)
Abstract	During the past few years, there has been a massive explosion of multimedia content such as un-annotated images on the web. Automatic image annotation is an important task for multimedia retrieval. By automatically allocating semantic concepts to un-annotated images, image retrieval can be performed over annotation concepts. In this work, we address the problem of automatic image annotation, namely automatically describing semantic content of image by concept classifier. Traditional approaches mainly consider the link between image and concept, but ignore the link between annotation concepts. We propose a novel Google Semantic link based image Annotation Model (GSAM), which can leverage the associated concept network (ACN) to enhance automatic semantic annotation performance. When several concepts appear in training set with high co-occurrence frequency, our model utilizes Google semantic link to increase the chances of predicting one concept if there is strong visual evidence for others. Additionally, the fusion between Google concept link and local concept link, and semantic links between single-concepts and multi-concepts are employed to improve annotation performance. In order to investigate the feasibility and effectiveness of our approach, we conduct experiments on Corel and IAPR datasets. The experimental results show that our approach considering semantic link outperforms existing state-of-the-art methods.
Starting Page	177
Ending Page	184
File Size	598121
Page Count	8
File Format	PDF
ISBN	9781479967155
DOI	10.1109/SKG.2014.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Google Visualization Vocabulary Correlation Semantics automatic semantic annotation Mathematical model machine learning semantic link Google distance
Content Type	Text
Resource Type	Article

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