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In defense of soft-assignment coding

Liu, Lingqiao; Wang, Lei; LIU, Xinwang

Description

In object recognition, soft-assignment coding enjoys computational efficiency and conceptual simplicity. However, its classification performance is inferior to the newly developed sparse or local coding schemes. It would be highly desirable if its classification performance could become comparable to the state-of-the-art, leading to a coding scheme which perfectly combines computational efficiency and classification performance. To achieve this, we revisit soft-assignment coding from two key...[Show more]

CollectionsANU Research Publications
Date published: 2011
Type: Conference paper
URI: http://hdl.handle.net/1885/31209
Source: Proceedings of IEEE International Conference on Computer Vision (ICCV 2011)
DOI: 10.1109/ICCV.2011.6126534

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