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Computer vision and machine learning have great potential to aid in aesthetic judgments and exploration, particularly in the understanding of shapes. This paper presents our work in a well-defined but largely unexplored problem in this field: the automated recognition of apparel silhouette attributes for real-world products. Silhouette attributes, such as v-neck for dresses and open toe for shoes,...
This paper presents methods to explore and fuse various clothing features for person clustering in family photos. Our approach automatically detects the clothing regions, extracts the global and localized features based on color and texture, and then computes multiple partitions of people based on different features, which are combined into the final consolidated person clustering with a cluster ensemble...
Automatic person clustering, which groups photos based on the individuals appearing in a photo collection, is a key component to facilitate photo management and sharing. Traditionally, person clusters are basically built by detecting faces and matching facial features. But these facial clusters can perform poorly when there are large pose variations and occlusions, which are not uncommon in consumer...
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