One step beyond bags of features: Visual categorization using components

The bag-of-visual-words (BoW) representation has received wide application and public acceptance for visual categorization. However, the histogram based image representation ignores the spatial information and correlations among visual words. To tackle these problems, in this paper, we propose to use some image regions called `components', as the higher-level visual elements to represent an image associating with the lower-level elements of `visual words'. Then we formulate the task of visual categorization into two progressive relationships among a given concept and the two-level visual elements of images, i.e., visual-words-to-components and components-to-concept. Firstly, component level linear SVM classifiers are learned to model the relationship between visual words and components, then the output of these SVM classifiers are linearly combined to model the relationships between components and concept. Experiments on the Scene-15 dataset and the Oxford Flowers dataset demonstrate the effectiveness of the proposed method.