DTCTH: a discriminative local pattern descriptor for image classification

Abstract

Despite lots of effort being exerted in designing feature descriptors, it is still challenging to find generalized feature descriptors, with acceptable discrimination ability, which are able to capture prominent features in various image processing applications. To address this issue, we propose a computationally feasible discriminative ternary census transform histogram (DTCTH) for image representation which uses dynamic thresholds to perceive the key properties of a feature descriptor. The code produced by DTCTH is more stable against intensity fluctuation, and it mainly captures the discriminative structural properties of an image by suppressing unnecessary background information. Thus, DTCTH becomes more generalized to be used in different applications with reasonable accuracies. To validate the generalizability of DTCTH, we have conducted rigorous experiments on five different applications considering nine benchmark datasets. The experimental results demonstrate that DTCTH performs as high as 28.08% better than the existing state-of-the-art feature descriptors such as GIST, SIFT, HOG, LBP, CLBP, OC-LBP, LGP, LTP, LAID, and CENTRIST.

Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors

“…A compact spatial pyramid-based image representation has been proposed for object and scene recognition (Elfiky et al, 2012). Discriminative Ternary Census Transform Histogram (DTCTH) has been proposed to capture the discriminative structural properties (Rahman et al, 2017) in an image. A sparse coding model (Yang et al, 2009) has been proposed for image classification along with maximum pooling based on a linear Spatial Pyramid Matching (SPM) method and Scale Invariant Feature Transform (SIFT).…”