Object co-segmentation aims to simultaneously segment common regions of interest from multiple images. It is of great importance to image classification, object recognition and image retrieval. One way to extract similar objects shared by multiple images is to construct a correlation function between image regions. In this paper, object co-segmentation is addressed based on weakly supervised data fusion. First, we integrate the image boundary information into weakly supervised clustering by adopting an efficient image segmentation algorithm with proved convergence. Feature learning as well as clustering are also incorporated into the proposed algorithm to establish a unified framework so that an optimal feature subspace with clustering-oriented methods is provided. Second, the shared object from multiple images is regarded as the procedure to search objects from heterogeneous data sources, which is formalized as data fusion problems. Using data fusion techniques, we present a novel method to evaluate the similarity between images, which facilitates the use of similar objects from multiple images. Finally, the two proposed object segmentation and co-segmentation algorithms are verified through publicly available datasets MSRA1000 and iCoseg. Experiments demonstrate that both algorithms are capable to achieve superior or comparable performance over the compared state-of-the-art segmentation methods in all tested datasets.