Fractal dimension of bag-of-visual words

Scene recognition is an important and challenging problem in computer vision. One of the most used scene recognition methods is the bag-of-visual words. Despite the interesting results, this approach does not capture the detail richness of spatial information of the visual words on the image. In this paper, we propose a new method to describe the visual words using the fractal dimension. Our method estimates the fractal dimension of each visual word on image through box-counting method. The fractal dimension is capable of providing complex and spatial information of the visual words in a simple and efficient way. We validate our method on three well-known scene and object datasets, and the experimental results reveal that our method leads to highly discriminative features of the visual words. In addition, the proposed method has achieved competitive results compared to popular methods in scene classification.