Object recognition based on background attributes

Attribute is the semantic description of an image, which denotes the existence or absence of a semantic property of the image, and it can not only be shape, material, part, category or functionality of an object, but also be label or context of a scene. To improve the accuracy of object classification, considering that object categories are related to the background where they belong to, an approach for object recognition based on modeling background attributes and foreground object attributes was proposed. Each attribute of background and object was trained by a support vector machine (SVM) classifier, and the output value of each attribute classifier was concatenated to form a new feature, based on which the final SVM classifier was trained. 10 kinds of background attributes were manually annotated for each image. Compared to the traditional method, method only based on object attributes and other methods considering different concatenating schemes of background and object features, experiments on the a-Pascal dataset show that the proposed method outperforms the others by around 2%, and background attributes can benefit object recognition task.