Image retrieval based on multi-semantic region weighting and multi-scale flatness weighting

The feature representation of images is the key to bridge the semantic gap and make computer understand images in the tasks of image retrieval. Semantic regions and salient targets are irreplaceable parts of cognitive images in image recognition. This paper proposes an unsupervised image retrieval method based on multi-semantic region weighting and multi-scale flatness weighting. Firstly, we divide the semantic regions by using the Full Convolutional Network (FCN) and calculate the multi-Semantic weight map (S-mask) to obtain the global features. Secondly, we introduce a flatness-weighted strategy to weight feature maps and aggregate the multi-scale features to obtain the local features. Finally, we cascade the global features and the local features to construct the final image representation. Experimental results on two widely-used databases demonstrate that the proposed method is effective and significantly outperforms the state-of-the-art retrieval methods.