Semi-supervised Learning for Large Scale Image Cosegmentation

Abstract

This paper introduces to use semi-supervised learning for large scale image co segmentation. Different from traditional unsupervised cosegmentation that does not use any segmentation ground truth, semi-supervised cosegmentation exploits the similarity from both the very limited training image foregrounds, as well as the common object shared between the large number of unsegmented images. This would be a much practical way to effectively co segment a large number of related images simultaneously, where previous unsupervised co segmentation work poorly due to the large variances in appearance between different images and the lack of segmentation ground truth for guidance in co segmentation. For semi-supervised co segmentation in large scale, we propose an effective method by minimizing an energy function, which consists of the inter-image distance, the intra-image distance and the balance term. We also propose an iterative updating algorithm to efficiently solve this energy function, which decomposes the original energy minimization problem into sub-problems, and updates each image alternatively to reduce the number of variables in each sub-problem for computation efficiency. Experiment results on iCoseg and Pascal VOC datasets show that the proposed co segmentation method can effectively co segment hundreds of images in less than one minute. And our semi-supervised co segmentation is able to outperform both unsupervised co segmentation as well as fully supervised single image segmentation, especially when the training data is limited.