In defense of soft-assignment coding

Lingqiao Liu*, Lei Wang, Xinwang Liu

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    451 Citations (Scopus)

    Abstract

    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 aspects: classification performance and probabilistic interpretation. For the first aspect, we argue that the inferiority of soft-assignment coding is due to its neglect of the underlying manifold structure of local features. To remedy this, we propose a simple modification to localize the soft-assignment coding, which surprisingly achieves comparable or even better performance than existing sparse or local coding schemes while maintaining its computational advantage. For the second aspect, based on our probabilistic interpretation of the soft-assignment coding, we give a probabilistic explanation to the magic max-pooling operation, which has successfully been used by sparse or local coding schemes but still poorly understood. This probability explanation motivates us to develop a new mix-order max-pooling operation which further improves the classification performance of the proposed coding scheme. As experimentally demonstrated, the localized soft-assignment coding achieves the state-of-the-art classification performance with the highest computational efficiency among the existing coding schemes.

    Original languageEnglish
    Title of host publication2011 International Conference on Computer Vision, ICCV 2011
    Pages2486-2493
    Number of pages8
    DOIs
    Publication statusPublished - 2011
    Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
    Duration: 6 Nov 201113 Nov 2011

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision

    Conference

    Conference2011 IEEE International Conference on Computer Vision, ICCV 2011
    Country/TerritorySpain
    CityBarcelona
    Period6/11/1113/11/11

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