6533b7d8fe1ef96bd126b825
RESEARCH PRODUCT
Hop: Histogram of patterns for human action representation
Liliana Lo PrestiMarco La CasciaVito Monteleonesubject
Apriori algorithmSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSeries (mathematics)Computer sciencebusiness.industryComputer Science (all)CodebookValue (computer science)Pattern recognition02 engineering and technologyAction classificationTheoretical Computer ScienceComputingMethodologies_PATTERNRECOGNITIONAction (philosophy)020204 information systemsHistogram0202 electrical engineering electronic engineering information engineeringFrequent pattern020201 artificial intelligence & image processingMultinomial distributionArtificial intelligenceHop (telecommunications)Representation (mathematics)businessdescription
This paper presents a novel method for representing actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. This paper proposes to learn a codebook of frequent sequential patterns by means of an apriori-like algorithm, and to represent an action with a Bag-of-Frequent-Sequential-Patterns approach. Preliminary experiments of the proposed method have been conducted for action classification on skeletal data. The method achieves state-of-the-art accuracy value in cross-subject validation.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 |