Learning Localized Perceptual Similarity Metrics for Interactive Categorization

Current similarity-based approaches to interactive finegrained categorization rely on learning metrics from holistic perceptual measurements of similarity between objects or images. However, making a single judgment of similarity at the object level can be a difficult or overwhelming task for the human user to perform. Secondly, a single general metric of similarity may not be able to adequately capture the minute differences that discriminate fine-grained categories. In this work, we propose a novel approach to interactive categorization that leverages multiple perceptual similarity metrics learned from localized and roughly aligned regions across images, reporting state-of-the-art results and outperforming methods that use a single nonlocalized similarity metric.

2015

Learning Localized Perceptual Similarity Metrics for Interactive Categorization

Wah, Catherine; Maji, Subhransu; Belongie, Serge

Learning Localized Perceptual Similarity Metrics for Interactive Categorization

IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, 2015.

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