Efficient image classification via sparse coding spatial pyramid matching representation of SIFT-WCS-LTP feature

Shape and texture information are critical to the accuracy of image classification systems. In this study, the authors propose a novel descriptor called weighted centre-symmetric local ternary pattern (WCS-LTP), better characterising the image local texture. Then, based on the proposed WCS-LTP descriptor, they introduce a new local scale invariant feature transform and WCS-LTP (SIFT-WCS-LTP) feature extraction approach. Compared with conventional local CS-LTP and SIFT features, the authors' proposed SIFT-WCS-LTP feature can not only capture the shape information of images, but also tend to extract more precise texture information. Finally, SIFT-WCS-LTP feature-based sparse coding spatial pyramid matching (ScSPM) representation classification is proposed for image classification. Extensive experimental results demonstrate that the effectiveness of their proposed SIFT-WCS-LTP feature-based ScSPM representation classification algorithm.