1. A method of class-unsupervised object segmentation, implemented at least in part by a computing device, the method comprising: converting one or more received images to grayscale images; extracting object parts from the grayscale images to identify a shape for an object class; clustering the extracted object parts into visual words, wherein the visual words describe a local appearance of an object class; iterating over the images being identified and matching the visual words to the extracted object parts by using a normalized grayscale correlation measure to identify a spatial relationship between pairs of the visual words by [vw i ,vw j ,d ij ˜N(μ ij ,σ ij )], wherein vw i and vw j represent the visual words and N(μ ij ,σ ij ) represents a Gaussian distribution fitting a distribution of a spatial distance d ij between the extracted object parts being matched to vw i and vw j ; oversegmenting the received images into superpixels, wherein the superpixels that are similar in color and texture are being grouped into subregions; and integrating top-down constraints from the object parts and bottom-up constraints from the superpixels on the shape for the object class to identify a relationship among the object parts and the superpixels.