Unsupervised Learning of Categories from Sets of Partially Matching Image Features
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چکیده
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into a space where they cluster according to their partial-match feature correspondences. After efficiently computing the pairwise affinities between the input images in this space, a spectral clustering technique is used to recover the primary groupings among the images. We introduce an efficient means of refining these groupings according to intra-cluster statistics over the subsets of features selected by the partial matches between the images, and based on a variable amount of user supervision. The output of the algorithm is a partition of the data into a set of learned categories, and a set of classifiers trained from these ranked partitions which can recognize the categories in novel images.
منابع مشابه
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into a space where they cluster according to their partial-match feature correspondences. After efficiently computing the pairwise affinities between the input images in this space, a spectral clustering technique is used ...
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