This paper considers a bipartite dictionary based salient object detection algorithm that assigns one of two labels (object/background) to each superpixel of an image. The algorithm will iteratively find for each of the labels two dictionaries referred to as the bipartite dictionary, and the dictionaries will in turn update the labels of the superpixels based on the assumption that features of a particular label is better represented by the dictionary of its own label than by the dictionary of the other label. This iteration stops when convergence is reached, in other words, when there is no update. An objective function is formulated such that the bipartite dictionary and superpixel labels maximize inter-class reconstruction error and simultaneously minimize intra-class reconstruction error. The proposed algorithm is evaluated on two benchmark datasets. Experimental results show that the proposed algorithm performs better than state-of-the-art algorithms for the dataset when the initial conditions are set appropriately. We have also found that the proposed algorithm tends to highlight salient objects more uniformly than other algorithms.