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Title: Multi-Label Learning for Semantic Image Annotation
Keywords: Image Annotation, Multi-Label Learning
Issue Date: 16-Apr-2013
Citation: CHEN XIANGYU (2013-04-16). Multi-Label Learning for Semantic Image Annotation. ScholarBank@NUS Repository.
Abstract: With the popularity of photo sharing websites, new web images on a wide variety of topics have been growing at an exponential rate. At the same time, the contents of images are also enriched and more diverse than ever before. This brings about two main challenging problems in semantic image annotation: 1) the semantic space of image dataset is enlarged and may contain two or more semantic spaces; 2) the trend of image corpus is towards large-scale or web-scale setting, which is generally unaffordable for traditional annotation approaches. To address the first challenging problem, this thesis proposes multi-label learning algorithms for semantic image annotation from two paradigms: multi-label learning on single-semantic space and multi-label learning on multi-semantic space. To address the second challenging problem, this thesis proposes an efficient sparse graph based multi-label learning scheme for large-scale image annotation, whereby both the efficacy and accuracy are further enhanced.
Appears in Collections:Ph.D Theses (Open)

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