KSR‐BOF: a new and exemplified method (as KSRs method) for image classification

Image classification is very important in pattern recognition and computer vision, where, for integrating final representation, feature pooling methods of the max‐pooling, sum‐pooling and average‐pooling have been widely used. In this study, the authors propose a new method called K ‐strongest responses (KSRs) on the dictionary atoms for integrating the coding coefficients to generate the final representation that is compared with the previous pooling methods, produces better performance for the image classification task. On the basis of the KSR method, to improve classification accuracy and generate more compact and discriminative final representation, a new framework consisting of two‐part KSR and bag‐of‐features is proposed. To evaluate the performance of the proposed method and framework, they apply it to locality‐constrained linear coding, linear distance coding and sparse coding by using two datasets from benchmarks of scene classification: 19‐class satellite scene and UC Merced Land. The results show that the coding coefficients integrated by their method and framework are more discriminative than other methods.