hLGP: A Modified Local Gradient Pattern for Image Classification

For image classification, Local Gradient Pattern (LGP) is an adaptive threshold-based feature descriptor which extracts the changes of intensities locally or globally of an image. This threshold is calculated by using Arithmetic Mean (AM) of gradient values of neighboring pixels. Due to using AM, the threshold value often unable to reduce outlier's effect. Hence some of the elements of an image are not identified properly. As a result, the discrimination capacity of LGP comparatively lower than other descriptors for several applications. Above this issue, we introduce a new gradient-based feature descriptor named as modified Local Gradient Pattern (hLGP) to overcome this problem of LGP. This paper shows the effective performance of hLGP on several applications like scene, flower, aerial, event, object image classifications which belong to some popular datasets and also show the experimental results which exhibited that hLG P performs comparatively better than LGP in those datasets.