LP²DH jointly hashes spatiotemporal pixel-difference vectors with locality preservation and Stiefel-manifold optimization to produce compact binary features that achieve state-of-the-art accuracy on UCLA, DynTex++, and YUPENN dynamic texture benchmarks.
Dynamic texture classification based on bag-of-models with mixture of student’s t-hidden Markov models,
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LP$^{2}$DH: A Locality-Preserving Pixel-Difference Hashing Framework for Dynamic Texture Recognition
LP²DH jointly hashes spatiotemporal pixel-difference vectors with locality preservation and Stiefel-manifold optimization to produce compact binary features that achieve state-of-the-art accuracy on UCLA, DynTex++, and YUPENN dynamic texture benchmarks.