Bag of Hierarchical Co-occurrence Features for Image Classification

We propose a bag-of-hierarchical-co-occurrence features method incorporating hierarchical structures for image classification. Local co-occurrences of visual words effectively characterize the spatial alignment of objects’ components. The visual words are hierarchically constructed in the feature space, which helps us to extract higher-level words and to avoid quantization error in assigning the words to descriptors. For extracting descriptors, we employ two types of features hierarchically: narrow (local) descriptors, like SIFT [1], and broad descriptors based on co-occurrence features. The proposed method thus captures the co-occurrences of both small and large components. We conduct an experiment on image classification by applying the method to the Caltech 101 dataset and show the favorable performance of the proposed method.