We compare the performance of Fisher vector encoding with sparse coding and locality constrained linear coding (LLC).
LOFESS can be considered as a method for learning sparse features with locality constraints to construct an occlusion-free feature space.
From the experiments and results, we observed that Fisher vector encoding is the most computationally efficient algorithm when compared to feature mapping using sparse coding and locality constrained linear coding (LLC).
Belief propagation [see (Meltzer et al., 2009) review and (Pearl, 1988) textbook] is an inference method for probabilistic graphical networks with sparse interdependence or locality.
Sparse representation, a locality-based data representation method, leads to promising results in many scientific and engineering fields.
Based on these pioneering works, aggregated vector-based methods emerged, including vector quantization [3], sparse coding [4], locality-constrained linear coding [5], and soft assignment [6].
In this paper, we present a locality-sensitive sparse representation toward optimized prototype classifier (LSROPC) for IAHCCR.
Locality-sensitive sparse representation based classification has been shown to be effective for in-air handwritten Chinese character recognition (IAHCCR).
The techniques under investigation are (a) local nonnegative matrix factorization (LNMF); (b) independent component analysis (ICA); (c) NMF with sparse constraints (NMFsc); (d) locality-preserving projections (Laplacian faces).
An extended berm of calcarenitic boulders is recognisable at Punta Saguerra, few kilometres south of Taranto (Apulia, Italy) while isolated boulders are sparse in other near localities.
It adaptively determines the locality based on sparse representation, instead of fixing the k-nearest neighbors in the original feature space.
