Unsupervised Fast Image Superpixel Generation With Guided Filtering

Deep learning has become one of the mainstream methods for image superpixel segmentation, most of which are supervised. The performance often depends on a large number of labeled data. Moreover, as a commonly used preprocessing procedure, speed and accuracy are two key indicators of the performance of superpixel segmentation algorithms. Hence, this paper proposes an unsupervised model to generate superpixels fast with accuracy. Firstly, using guided filtering to design a downsampling-upsampling framework to improve the inference speed of suprpixel segmentation model. Then, multi-scale attention mechanism is adopted to improve the accuracy of superpixel segmentation. At the same time, a robust loss function is defined based on image information. In this way, an unsupervised image superpixel generation module was proposed; Finally, a sequential training strategy is adopted to reduce the model's dependence on a large amount of training data. The experimental results tested on the public datasets BSDS500 and DRIVE show that the proposed model has comparable accuracy with supervised algorithms, outperforms existing unsupervised methods, and achieves fast performance.