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Don't Look Back: Post-hoc Category Detection via Sparse Reconstruction

Authors:
Song, Hyun Oh
Fritz, Mario
Althoff, Tim
Darrell, Trevor
Technical Report Identifier: EECS-2012-16
January 24, 2012
EECS-2012-16.pdf

Abstract: 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 matrix-vector 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.