Toward sparse coding on cosine distance

Jonghyun Choi, Hyunjong Cho, Jungsuk Kwac, Larry S. Davis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


Sparse coding is a regularized least squares solution using the L1 or L0 constraint, based on the Euclidean distance between original and reconstructed signals with respect to a predefined dictionary. The Euclidean distance, however, is not a good metric for many feature descriptors, especially histogram features, e.g. many visual features including SIFT, HOG, LBP and Bag-of-visual-words. In contrast, cosine distance is a more appropriate metric for such features. To leverage the benefit of the cosine distance in sparse coding, we formulate a new sparse coding objective function based on approximate cosine distance by constraining a norm of the reconstructed signal to be close to the norm of the original signal. We evaluate our new formulation on three computer vision datasets (UCF101 Action dataset, AR dataset and Extended YaleB dataset) and show improvements over the Euclidean distance based objective.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479952083
Publication statusPublished - 2014 Dec 4
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 2014 Aug 242014 Aug 28

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference22nd International Conference on Pattern Recognition, ICPR 2014

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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