Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.knosys.2018.12.010 | Knowledge-Based Systems |
Keywords | Field | DocType |
00-01,99-00 | Spatial relation,Graph,Data mining,Automatic image annotation,Annotation,Computer science,Semantic gap,Matching (graph theory),Correlation,Exponential growth | Journal |
Volume | ISSN | Citations |
166 | 0950-7051 | 0 |
PageRank | References | Authors |
0.34 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Zhang | 1 | 8 | 4.15 |
| Zhenkun Wang | 2 | 1 | 1.03 |
| Yakun Mu | 3 | 0 | 0.34 |
| Zhe Wang | 4 | 50 | 20.04 |