In defense of soft-assignment coding
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]
|Collections||ANU Research Publications|
|Source:||Proceedings of IEEE International Conference on Computer Vision (ICCV 2011)|
|01_Liu_In_defense_of_soft-assignment_2011.pdf||222.15 kB||Adobe PDF||Request a copy|
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