Knowing Verb From Object: Retagging With Transfer Learning on Verb-Object Concept Images
Image retagging is significant and essential for tag-based applications, such as search and browsing. However, most existing image retagging approaches are typically based on enriching-and-removing and/or reranking strategies, which lead to two drawbacks: 1) since the object and/or human appeared in the images are tagged as individuals, the meanings represented by the mutual context of object and human are ignored and not tagged, and 2) some images which are visually dissimilar but semantically similar could be filtered incorrectly, as they are conflict with the content consistency rule. These two defects are distinct especially when images with human-object interactions are retagged. To tackle these defects, in this paper we propose a Bayesian approach to jointly consider the human and object in an image and retag it properly. In our approach, human and objects in images are detected and their interrelationships are taken into account. Tags which represent the mutual context of human and objects are then mapped to those interrelationships by a probabilistic graphical model. For a new image which lacks the tag representing the interaction between human and object, our model can correctly retag it for the interaction. In this paper, those images involving human-object interactions are called verb-object concept images, and experiments on a 60-class dataset demonstrate the capacity of our Bayesian retagging approach of verb-object concept images (BRVOI).