Featureless: Bypassing feature extraction in action categorization
This work addresses action recognition for computer vision applications, but it is incremental as it adapts existing methods to a new domain.
The paper tackles action categorization by learning video representations directly from raw data, bypassing traditional feature extraction, and demonstrates its efficiency on the UCF11 dataset with competitive performance.
This method introduces an efficient manner of learning action categories without the need of feature estimation. The approach starts from low-level values, in a similar style to the successful CNN methods. However, rather than extracting general image features, we learn to predict specific video representations from raw video data. The benefit of such an approach is that at the same computational expense it can predict 2 D video representations as well as 3 D ones, based on motion. The proposed model relies on discriminative Waldboost, which we enhance to a multiclass formulation for the purpose of learning video representations. The suitability of the proposed approach as well as its time efficiency are tested on the UCF11 action recognition dataset.