Image Auto-annotation with Graph Learning

It is important to integrate contextual information in order to improve the performance of automatic image annotation. Graph based representations allow incorporation of such information. In this paper, we propose a graph-based approach to automatic image annotation which models both feature similar...

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Bibliographic Details
Main Authors: Yu Tang Guo, Bin Luo
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algorithm design and analysis
Complexity theory
Equations
fast solution
graph learning
image annotation
Mathematical model
Random walk with restart
Semantics
Training
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Summary:It is important to integrate contextual information in order to improve the performance of automatic image annotation. Graph based representations allow incorporation of such information. In this paper, we propose a graph-based approach to automatic image annotation which models both feature similarities and semantic relations in a single graph. The annotation quality is enhanced by introducing graph link weighting techniques based on inverse document frequent and the similarity of the word based on Co-occurrence relation in the training set . According to the characteristics of in ear correlation, block-wise and community-like structure in the modeled graph, we divide the graph into several sub graphs and approximate high rank adjacent matrix of the graph by using low rank matrix. Thus, we can achieve image annotation quickly. Experimental results on the Corel image dabasets show the effectiveness of the proposed approach in terms of performance.
DOI:10.1109/AICI.2010.171