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Semantic embedding for indoor scene recognition by weighted hypergraph learning
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DOI：10.1016/j.sigpro.2014.07.027.png)
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Conventional methods for indoor scenes classification is a challenging task due to the gaps between images' visual features and semantics. These methods do not consider the interactions among features or objects. In this paper, a novel approach is proposed to classify scenes by embedding semantic information in the weighted hypergraph learning. First, hypergraph regularization is improved by optimizing weights of hyperedges. Second, the connectivity among images is learned by statistics of objects appearing in the same image. In this way, semantic gap is narrowed. The experimental results demonstrate the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Semantic information
Indoor scenes classification
Attributes learning
Weighted hypergraph
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