Image retrieval based on multi-instance saliency model

Existing methods for visual saliency based image retrieval typically aim at single instance image. However, without any prior knowledge, the content of single instance image is ambiguous and these methods cannot effectively reflect the object of interest. In this paper, we propose a novel image retrieval framework based on multi-instance saliency model. First, the feature saliency is computed based on global contrast, local contrast and sparsity, and the synthesize saliency map is obtained by using Multi-instance Learning (MIL) algorithm to dynamically weight the feature saliency. Then we employ a fuzzy region-growth algorithm on the synthesize saliency map to extract the saliency object. We finally extract color and texture feature as the retrieval feature and measure feature similarity by Euclidean distance. In the experiments, the proposed method can achieve higher multi-instance image retrieval accuracy than the other single instance image retrieval methods based on saliency model.