Abstract
A metric for natural image patches is an important tool for analyzing images. An efficient means of learning one is to train a deep network to map an image patch to a vector space, in which the Euclidean distance reflects patch similarity. Previous attempts learned such an embedding in a supervised manner, requiring the availability of many annotated images. In this paper, we present an unsupervised embedding of natural image patches, avoiding the need for annotated images. The key idea is that the similarity of two patches can be learned from the prevalence of their spatial proximity in natural images. Clearly, relying on this simple principle, many spatially nearby pairs are outliers. However, as we show, these outliers do not harm the convergence of the metric learning. We show that our unsupervised embedding approach is more effective than a supervised one or one that uses deep patch representations. Moreover, we show that it naturally lends itself to an efficient self-supervised domain adaptation technique onto a target domain that contains a common foreground object.
Highlights
Humans can understand what they see in different regions of an image, or tell whether two regions are similar or not
To keep such encoding generic, they are not predetermined by certain classes, but instead aim to project image patches into an embedding space, where Euclidean distances correlate with general similarity among image patches
We introduce an unsupervised patch embedding method, which analyses natural image patches to define a mapping from a patch to a vector, such that the Euclidean distance between two vectors reflects their perceptual similarity
Summary
Humans can understand what they see in different regions of an image, or tell whether two regions are similar or not. Despite recent progress, such forms of image understanding remain extremely challenging. Image understanding can be formalized as the ability to encode contents of small image patches into representation vectors. To keep such encoding generic, they are not predetermined by certain classes, but instead aim to project image patches into an embedding space, where Euclidean distances correlate with general similarity among image patches. As natural patches form a low dimensional manifold in the space of patches [1, 2], such an embedding of image patches allows various image understanding and segmentation tasks. Semantic segmentation is reduced to a simple clustering technique based on l2 distances
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