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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,...
Current work in object categorization discriminates among objects that typically possess gross differences which are readily apparent. However, many applications require making much finer distinctions. We address an insect categorization problem that is so challenging that even trained human experts cannot readily categorize images of insects considered in this paper. The state of the art that uses...
Visual dictionaries are widely employed in object recognition to map unordered bags of local region descriptors into feature vectors for image classification. Most visual dictionaries have been constructed by unsupervised clustering. This paper presents an efficient discriminative approach, called iterative discriminative clustering (IDC), for dictionary learning. In this approach, each dictionary...
Visual dictionaries have been successfully applied to ??bags-of-points?? image representations for generic object recognition. Usually the choice of low-level interest region detector and region descriptor (channel) has significant impact on the performance of visual dictionaries. In this paper, we propose a discriminative evaluation method-Maximum Mutual Information (MMI) curves to analyze the properties...
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