Inductive hierarchical nonnegative graph embedding for “verb–object” image classification

Most existing image classification algorithms mainly focus on dealing with images with only “object” concepts. However, in real-world cases, a great variety of images contain “verb–object” concepts, rather than only “object” ones. The hierarchical structure embedded in these “verb–object” concepts can help to enhance classification. However, traditional feature representation methods cannot utilize it. To tackle this problem, we present in this paper a novel approach, called inductive hierarchical nonnegative graph embedding. By assuming that those “verb–object” concept images which share the same “object” part but different “verb” part have a specific hierarchical structure, we integrate this hierarchical structure into the nonnegative graph embedding technique, together with the definition of inductive matrix, to (1) conduct effective feature extraction from hierarchical structure, (2) easily transfer each new testing sample into its low-dimensional nonnegative representation, and (3) perform image classification of “verb–object” concept images. Extensive experiments compared with the state-of-the-art algorithms on nonnegative data factorization demonstrate the classification power of proposed approach on “verb–object” concept images classification.