Image region label refinement using spatial position relation graph
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摘要
With the exponential growth of massive image data, automatic image annotation is becoming more important in image management and retrieval. Traditional image region annotation methods, through machine learning and low-level visual features, typically yield incorrect annotation results owing to the influence of the Semantic Gap. We herein propose a novel label refinement method for improving the image region annotation results. A spatial position relation graph with co-occurrence relations and spatial position relations among labels is proposed to analyze the latent semantic correlations among image region labels. Moreover, an incremental iterative random-walking algorithm is proposed to reconstruct the region relation graph for detecting non-dependable regions whose labels do not fit the semantic context of an image. Subsequently, a graph matching algorithm with semantic correlation and spatial relation analysis is proposed for non-dependable region label completion. Experiments on Corel5K demonstrate that our proposed spatial-position-relation-graph- based label refinement method can achieve good performance for image region label refinement.
论文关键词:00-01,99-00,Image region annotation,Label refinement,Spatial position relation graph,Random-walking,Graph matching
论文评审过程:Received 12 April 2018, Revised 4 December 2018, Accepted 8 December 2018, Available online 13 December 2018, Version of Record 23 January 2019.
论文官网地址:https://doi.org/10.1016/j.knosys.2018.12.010