Foreground Focus: Unsupervised Learning from Partially Matching Images |
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Authors: | Yong Jae Lee Kristen Grauman |
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Affiliation: | (1) Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX 78712, USA;(2) Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712, USA |
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Abstract: | We present a method to automatically discover meaningful features in unlabeled image collections. Each image is decomposed
into semi-local features that describe neighborhood appearance and geometry. The goal is to determine for each image which
of these parts are most relevant, given the image content in the remainder of the collection. Our method first computes an
initial image-level grouping based on feature correspondences, and then iteratively refines cluster assignments based on the
evolving intra-cluster pattern of local matches. As a result, the significance attributed to each feature influences an image’s
cluster membership, while related images in a cluster affect the estimated significance of their features. We show that this
mutual reinforcement of object-level and feature-level similarity improves unsupervised image clustering, and apply the technique
to automatically discover categories and foreground regions in images from benchmark datasets. |
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Keywords: | |
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