Joint object discovery and segmentation with image-wise reconstruction error

We tackle the problem of joint discovery and segmentation of the object of interest from noisy image sets collected via web crawling (e.g., Figure 1). Existing methods [1] [2] [3] employ region-wise comparison in order to separate noise images (images not containing target objects) from the rest, which may be a bottleneck for scaling up to larger datasets. Our idea to avoid such computationally intensive operations is to use image-wise reconstruction errors. Specifically, based on the assumption that images containing target objects are easier to be reconstructed by a pool of target objects than noise images, we first reconstruct each image using a small number of similar target objects. The resulting error is then combined with some other criteria (e.g., saliency) so as to delineate only target object regions. Experimental evaluations on a noisy image dataset [1] demonstrate that our approach achieves state-of-the-art results on every subset of the dataset 5-7 times faster than existing methods.