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Canonical Image Selection by Visual Context Learning

Canonical image selection is to select a subset of photos that best summarize a photo collection. In this paper, we define the canonical image as those that contain most important and distinctive visual words. We propose to use visual context learning to discover visual word significance and develop Weighted Set Coverage algorithm to select canonical images containing distinctive visual words. Experiments with web image datasets demonstrate that the canonical images selected by our approach are not only representatives of the collected photos, but also exhibit a diverse set of views with minimal redundancy.