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.

中文翻译：

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KSR-BOF：一种新的示例性图像分类方法（作为KSRs方法）

图像分类在模式识别和计算机视觉中非常重要，其中，为了集成最终表示，已广泛使用最大池，求和池和平均池的特征池方法。在这项研究中，作者提出了一种称为ķ 字典原子上最强的响应（KSR），用于整合编码系数以生成最终表示形式，并将其与以前的合并方法进行比较，从而为图像分类任务提供了更好的性能。在KSR方法的基础上，为提高分类的准确性，产生更紧凑，更具判别性的最终表示，提出了一个由两部分KSR和特征包组成的新框架。为了评估所提出的方法和框架的性能，他们使用场景分类基准中的两个数据集将其应用于位置受限的线性编码，线性距离编码和稀疏编码：19类卫星场景和UC Merced Land。结果表明，与其他方法相比，通过其方法和框架综合得到的编码系数具有更大的判别力。