基于区域非均匀空间采样特征的图像分类方法

大量实验证明抽取图像中稠密局部特征能够大大提高图像分类性能，目前的常用策略是基于空间均匀密集采样来实现稠密局部特征的抽取。该文提出一种新的基于区域非均匀空间采样的局部特征抽取方法。首先，用过分割技术对原始图像进行分割，从而得到图像的分割区域，并采用显著性检测技术估计每个过分割区域的重要性。然后，在保证不增加采样数的情况下，对重要的显著性区域的边界实行密集均匀采样，对区域内部根据区域大小和重要性实行随机采样。最后，采用词袋表示模型来实现图像分类。在两个广泛应用的数据库，8类体育运动(UIUC Sports)和256类自然图像(Caltech-256)数据库进行实验。实验结果证明，该文提出的采样策略进一步提高了基于稠密局部特征的图像分类性能。

Image Classification Based on Region Non-uniform Spatial Sampling

Extensive experiments demonstrate that locally dense features are able to improve greatly performances of image classification, and the popular way is to conduct spatially uniform sampling for locally dense feature extraction. In this paper, a new method to extract locally dense features, region-based non-uniform spatial sampling is proposed to improve further the performance of image classification. Firstly, an over-segmentation operator is performed on the image, and then a saliency detection method is applied to estimate the importance of each segmented region. To keep the same sampling number of local features, the dense features are extracted along the boundary of the important salient region with dense sampling, as well as inside the region with random sampling according to its area and importance. Finally, the Bog-of-Words representation model is used for image classification. Extensive experiments are conducted on two widely-used datasets (UIUC Sports and Caltech-256). The experimental results show that proposed sampling strategy obtains an efficient performance.