Unsupervised discovery of mid-level discriminative patches
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Ensemble partitioning for unsupervised image categorization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Objects vary in their visual complexity, yet existing discovery methods perform "batch" clustering, paying equal attention to all instances simultaneously - regardless of the strength of their appearance or context cues. We propose a self-paced approach that instead focuses on the easiest instances first, and progressively expands its repertoire to include more complex objects. Easier regions are defined as those with both high likelihood of generic objectness and high familiarity of surrounding objects. At each cycle of the discovery process, we re-estimate the easiness of each subwindow in the pool of unlabeled images, and then retrieve a single prominent cluster from among the easiest instances. Critically, as the system gradually accumulates models, each new (more difficult) discovery benefits from the context provided by earlier discoveries. Our experiments demonstrate the clear advantages of self-paced discovery relative to conventional batch approaches, including both more accurate summarization as well as stronger predictive models for novel data.