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Image region label refinement using spatial position relation graph
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DOI：10.1016/j.knosys.2018.12.010.png)
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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. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Image region annotation
Label refinement
Spatial position relation graph
Random-walking
Graph matching
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7.6
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1.3W
被引数:
4.5W
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