Abstract
In this paper, we propose a novel multi-label image annotation for image retrieval based on annotated keywords. For multi-label image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image is segmented into several regions with image segmentation algorithm, pre-trained SVMs are used to annotate each region, final label is obtained by merging all the region labels. A novel annotation refinement approach based on PageRank is proposed to get rid of irrelevant labels. Based on multi-label of image, image retrieval system provides keyword-based image retrieval service. Multi-labels can provide abundant descriptions for image content in semantic level, and experiment results shows the multi-label annotation algorithm can improve precision and recall of image retrieval.
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