Hierarchicalword image representation for parts-based object recognition
Many multimedia applications can benefit from recognizing image content. It requires a robust and discriminative representation of objects, especially in the situation of only a few training samples available. In this paper, we present a new approach to integrate the advantages of bag-of-words model and part-based model for image recognition. Each image is encoded as a Hierarchical Word Image (HWI), which contains not only visual appearance but also spatial information. The object parts are then located and represented in HWI. Finally, the part-based Star Model (SM) is used to learn the object model and recognize the test images. It is shown that our proposed approach can detect more accurate part candidates and significantly improve the performance of original part-based model for object recognition.