Verb-Object Concepts Image Classification via Hierarchical Nonnegative Graph Embedding

摘要

Most existing image classification methods focus on handling images with only "object" concepts. At the same time, in real-world cases, there exists a great variety of images which 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 representing methods cannot utilize it. To tackle this defect, we present in this paper a novel approach, called Hierarchical Nonnegative Graph Embedding (HNGE). By assuming that those "verb-object" concept images which share the same "object" part but different "verb" part have a specific hierarchical structure, we make use of this hierarchical structure and employ an effective technique, named nonnegative graph embedding, to perform feature extraction as well as image classification. Extensive experiments compared with the state-of-the-art algorithms on nonnegative data factorization demonstrate the feasibility, convergency and classification power of proposed approach on "verb-object" concept images classification.