A Novel Image Classification Method Based on Bag-of-Words Framework

In this paper, a novel image classification method is proposed with a Bag-of-Words framework. For its simplified characteristics, the Bag-of-Words method is widely used in fields such as image classification and object recognition. Nevertheless, it still has some challenges, e.g., one single feature cannot always describe the information of a whole image, and the visual words allocation mechanism cannot represent the image better. Accordingly, two methods are proposed in this paper. For the first problem, a feature-fusion method is used by combining SIFT and HOG features. For the second, a soft threshold visual word allocation mechanism is put forward, in which k-means method is adopted to cluster the syncretic feature and form a “Visual Dictionary“, where each of the visual words with different weights is assigned to. Meanwhile, to add the image's spatial information, SPM theory is combined within the Bag-of-Words framework by using an SVM classifier to classify the image. Experiments on the Caltech-101 and Scene-15 datasets show that the proposed method outperforms the traditional methods.