Salience based hierarchical fuzzy representation for object recognition

Object recognition is one of the most popular tasks in computer vision. Various works have been reported in recent years. Bag of visual words (BOVW) is shown to be an effective model for this task. BOVW extracts features or visual words from the whole image. However, we know there are two kinds of information in an image: objects and background. Obviously, the features in the objects and background are not equivalently important for recognizing the object. In this paper, we propose a novel solution to considering the different regions. Intuitively speaking, the object in an image is more salient than the background. Based on this observation, we use a salience detection technique to divide an image into three regions: objects, boundary and background. Then we extract features from these regions, respectively. The features are finally integrated with an multi-kernel SVM classifier. We conduct experiments on some datasets, and the results show that our approach outperforms the traditional BOVW and weighted BOVW.