(a) Images downloaded from the Internet (b) Our automatic segmentation results (c) State-of-the-art co-segmentation results [8] Image search Object discovery Figure 1 . Image datasets collected from Internet search vary considerably in their appearance, and typically include many noise images that do not contain the object of interest (a small subset of the car image dataset is shown in (a); the full dataset is available in the accompanying material). Our algorithm automatically discovers and segments out the common object (b). Note how no objects are discovered for noise images in (b). Most previous co-segmentation methods, in contrast, are designed for more homogeneous datasets in which every image contains the object of interest, and, therefore, their performance degrades in the presence of noise (c). We present a new unsupervised algorithm to discover and segment out common objects from large and diverse image collections. In contrast to previous co-segmentation methods, our algorithm performs well even in the presence of significant amounts of noise images (images not containing a common object), as typical for datasets collected from Internet search. The key insight to our algorithm is that common object patterns should be salient within each image, while being sparse with respect to smooth transformations across images. We propose to use dense correspondences between images to capture the sparsity and visual variability of the common object over the entire database, which enables us to ignore noise objects that may be salient within their own images but do not commonly occur in others. We performed extensive numerical evaluation on established co-segmentation datasets, as well as several new datasets generated using Internet search. Our approach is able to effectively segment out the common object for diverse object categories, while naturally identifying images where the common object is not present.
REF explore object discovery using Internet images.
Unsupervised Joint Object Discovery and Segmentation in Internet Images
2013 IEEE Conference on Computer Vision and Pattern Recognition
2013 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science