Social image platforms allow their users sharing and searching their photos based on images' tags. These tags are provided by different users. Inevitably, the tags are spontaneously ambiguous, and personalized. So, learning the relevance between tags and images is playing an important role in tag-based retrieval systems. Choosing visual neighbors for seed images as voters is a widely used method for learning tag relevance. However, most existing methods of choosing visual neighbors for seed images are based on the global features of the whole images, ignoring the local features. In this paper ...
