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A review on automatic image annotation techniques
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DOI：10.1016/j.patcog.2011.05.013.png)
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摘要
En 中文
Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Image retrieval
Machine learning
Semantic gap
Image annotation
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Texture
Shape
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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