Locality-constrained and spatially regularized coding for scene categorization

DTCTH: a discriminative local pattern descriptor for image classification

“…ScSPM performs better than both linear SPM kernel (LSPM) on histograms and traditional nonlinear SPM kernels with linear SVM (LSVM) because the pooling of sparse codes quantizes only the essential features which is linearly separable by SVM. However, ScSPM solves L1-norm optimization problem which is computationally expensive [61]. Moreover, it is non-consistent to encode similar descriptors [61,62].…”

“…Moreover, it is non-consistent to encode similar descriptors [61,62]. Several modifications have been proposed for these problems [61][62][63]. For instance, Wang et al propose a modification of ScSPM by considering locality constraints in linear coding (LLC) to project each descriptor into its local-coordinate system where projected coordinates are amalgamated by MP [62].…”

“…However, ScSPM solves L1-norm optimization problem which is computationally expensive [61]. Moreover, it is non-consistent to encode similar descriptors [61,62]. Several modifications have been proposed for these problems [61][62][63].…”

“…Most of the sparse coding techniques [60,62] are adopted on local features independently which consider the global similarity by constraint sparsity. However, dense local features share some local contextual information which is discarded by the existing sparse codingbased techniques and become less reliable when adopting spatial pooling [61]. To address this problem, a localityconstrained and spatially regularized (LCSR) coding is proposed by considering local spatial context of an image into the usual coding strategies which preserves locality constraints both in the feature space and spatial domain of the image [61].…”

“…However, dense local features share some local contextual information which is discarded by the existing sparse codingbased techniques and become less reliable when adopting spatial pooling [61]. To address this problem, a localityconstrained and spatially regularized (LCSR) coding is proposed by considering local spatial context of an image into the usual coding strategies which preserves locality constraints both in the feature space and spatial domain of the image [61]. The information loss in the feature quantization through pooling is still found, though several coding methods are introduced to address this problem.…”