Abstract

We propose a novel method for unsupervised class segmen- tation on a set of images. It alternates between segmenting ob ject in- stances and learning a class model. The method is based on a segmen- tation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmenta- tion. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in ob ject class detection. Our method is inspired by inter- active segmentation methods [1], but it is fully automatic and learns models characteristic for the ob ject class rather than specific to one par- ticular ob ject/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) trans- fers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive per- formance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].