Color image segmentation by fixation-based active learning with ELM

The human vision observes an image by making a series of fixations. In fixation, our eyes continually tremble, which is called the microsaccades that may reflect an optimal sampling strategy and spatiotemporal characteristics. Although the decrease in microsaccade magnitude leads to visual fading in our brain that may provide a mechanism to shift fixation. This paper proposes an iterative framework for figure-ground segmentation by sampling-learning via simulating human vision. First, fixation-based sampling is utilized to get a few positive and negative samples. A pixels classifier based on the RGB color could be trained by ELM (extreme learning machine) algorithm, which not only extracts object regions, but also provides a reference boundary of objects. Then, the boundary of object region could be refined by minimizing graph cut. The region of refined object can be re-sampled to provide more accurate samples/pixels involved object and background for the next training. The iteration would convergence when the pixel classifier gets stable segmentation result continually. Based on the ELM algorithm, the proposed method run faster than state-of-the-art method, and can cope with the complexity and uncertainty of the scene. Experimental results demonstrate the learning-based method could reliably segment multiple-color objects from complex scenes.