关键词：模型框架原型;类级别检索;穷举搜索;稀疏重建方法;
摘 要：We consider optimal representations for representing prototypical categories in the latent deformable part model framework, with a specific emphasis on category-level retrieval tasks defined “on the fly” for a large corpus. In this
setting, it is impractical to perform an exhaustive search with a full model; we investigate methods which approximately reconstruct the score function of a novel category from a set of precomputed responses. We propose a novel
sparse reconstruction method where part classifiers are decomposed via a shared dictionary of part filters; in turn, our method can efficiently reconstruct approximate part responses on large image corpora using a sparse matrixvector product based on pre-computed filter responses instead of exhaustive convolutions. We compare our method to baseline schemes using SVD-based or nearest-category approximation and show our method is more effective at detecting novel categories. We additionally demonstrate results towards an end-to-end system for activity detection which trains a protoype category concept model from one dataset (PASCAL), learns post-hoc categories on the fly based on training data from a second dataset where labeled data are available (ImageNet), and sucessfully detects instances
in test data from a third dataset (TRECVID MED) via reconstruction with the precomputed prototype models.
索取原文[10 页]
027-87841330