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Properly utilizing the spatial correlation of regions benefits for improving the performance of label localization task. However, we could not obtain this information directly since we do not have the region level ground truth. In this paper, we propose a weakly spatial constrained graph propagation by mining the spatial correlation from unlabeled regions and integrating it into the graph propagation...
With the permeation of Web 2.0, large-scale user contributed images with tags are easily available on social websites. How to align these social tags with image regions is a challenging task while no additional human intervention is considered, but a valuable one since the alignment can provide more detailed image semantic information and improve the accuracy of image retrieval. To this end, we propose...
The bag-of-visual-words (BoW) representation has received wide application and public acceptance for visual categorization. However, the histogram based image representation ignores the spatial information and correlations among visual words. To tackle these problems, in this paper, we propose to use some image regions called ‘components’, as the higher-level visual elements to represent an image...
Currently, the bag of visual words (BOW) representation has received wide applications in object categorization. However, the BOW representation ignores the dependency relationship among visual words, which could provide informative knowledge to understand an image. In this paper, we first design a simple method to discover this dependency through computing the spatial correlation between visual words...
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