Details

Autor(en) / Beteiligte

• Bida, Ikram
• Aouat, Saliha

Titel

Fast dynamic texture recognition based on block estimation and axial spatio-temporal motion vector components

Ist Teil von

• Signal, image and video processing, 2023-06, Vol.17 (4), p.1511-1519

Ort / Verlag

London: Springer London

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Current dynamic texture motion-based features are practically all pixel-based signatures, therefore making the recognition slow and favoring the quality over computational speed, which is unreasonable for nowadays time-sensitive applications. Consequently, the goal of this work is to ensure a balanced accuracy and computational speed of recognition by exploring block-based motion features that are more likely adaptable for fast characterization rather than pixel-based. In this paper, we proposed four fast and innovative block-based motion approaches that characterize dynamic texture raw videos without any further segmentation for recognition purposes. Their originality is to adopt block motion estimation fast algorithms and introduce the novel Axial Spatio-temporal Motion Vector Components to be analyzed statistically using customized space-time texture features (first-order, second-order, and higher-order). Experimentations and results yielded a satisfying equilibrium between recognition and computational speed on multiple Dynamic texture datasets: DynTex, DynTex++ and UCLA compared to the pixel-based techniques with a high reduction of calculation time.