Fully convolutional neural network combined with K-means clustering algorithm for image segmentation

Image segmentation is an important part of many computer vision tasks such as image recognition and image understanding. Traditional image segmentation algorithms are susceptible to the influence of complex backgrounds such as illumination, shading and occlusion, thus the application of convolution neural network to image segmentation becomes a hot spot of current research. But in the process of image convolution, as the convolution goes further, the image will lose some edge information, resulting in the blurring of the final partition edge. To overcome this problem, we propose an image segmentation algorithm combining the fully convolution neural network and K-means clustering algorithm. By conducting pixel matching between the coarse segmentation result obtained by using the convolution neural network and the segmentation results obtained by using K-means, the algorithm enhances the classification of pixels on the edge to improve segmentation accuracy. The proposed algorithm adopts two-stage training method to train and optimize the model. The experimental results on VOC2012 set validate the effectiveness of the proposed method.