Dynamic texture recognition using multiscale PCA-learned filters

In this paper, we propose a novel method for dynamic texture recognition using multiscale PCA-learned filters. PCA is utilized to learn multiscale filters from image sequences on three orthogonal planes (XY, XT and YT). Filter responses that contain both spatial and temporal information at multiple scales are then encoded into a descriptor named MPCAF-TOP. The proposed method is simple to derive and implement, and also very effective for dynamic texture recognition. The proposed method is evaluated on two benchmark databases, namely UCLA and DynTex++. Experimental results show that the proposed approach is comparable to state-of-the-art methods.