A Hierarchical Feature Extraction Scheme with Special Vocabulary Generation for Natural Scene Classification

To automatically classify natural scenes instead of manual ways, this paper proposes a novel approach to recognize scene categories. First, we extract appearance features from an image similar to a pyramid. Then, the visual words are generated from different classes separately based on Bag of Words (BOW) model. At last, Spatial Pyramid Matching (SPM) algorithm is used to obtain histogram of visual words and Support Vector Machine (SVM) is applied to classification. There are two contributions in this paper: one is that we partition an image into patches at different resolution levels and use multiple descriptors to obtain some omissive image information; the other is that visual words are formed by performing K-means clustering from each category and concatenated to form a dictionary distinguish to traditional BOW. We present satisfactory performances on a large scale of 13 categories dataset.