The Bag-of-Words (BoW) model-commonly used for image classification-has two strong limitations: on one hand, visual words are lacking of explicit meanings, on the other hand, they are usually polysemous. This paper proposes to address these two limitations by introducing an intermediate representation based on the use of semantic attributes. Specifically, two different approaches are proposed. Both approaches consist in predicting a set of semantic attributes for the entire images as well as for local image regions, and in using these predictions to build the intermediate level features. Experiments on four challenging image databases (PASCAL VOC 2007, Scene-15, MSRCv2 and SUN-397) show that both approaches improve performance of the BoW model significantly. Moreover, their combination achieves the state-of-the-art results on several of these image databases.
For instance, REF proposed to address two limitations of BOVW model: the lack of explicit meanings of visual words and the visual words are usually polysemous.
Improving Image Classification Using Semantic Attributes
International Journal of Computer Vision
International Journal of Computer Vision
Computer Science