一种基于协方差描述子的场景分类算法

A algorithm for scene classification based on covariance descriptor

图像场景分类一直是计算机视觉领域的一个热点问题。提出了协方差描述子场景分类算法,它聚合了像素位置、颜色特征、方向特征和局部纹理特征等互补特征形成协方差描述子。为了避免计算黎曼空间内的协方差距离测度,把协方差描述子转换成欧式空间内的Sigma点特征,可以实现线性的场景描述和支持向量机训练。在SUN Database标准数据集上进行了算法分类测试,并与经典的场景分类算法进行了性能比较;通过构造包含噪声的场景数据集,验证了新算法和经典算法的鲁棒性。实验结果表明该算法在计算效率和分类性能方面具有很强优势,同时具有较好的噪声鲁棒性。

Scene classification is a hot topic in computer vision. Under the premise of image segmentation, a novel scene classification algorithm is proposed, which combines pixel location, color characteristics, direction features and lo- cal texture features to form the covariance descriptor. To avoid computing tedious distance measure in Riemannian space, the covariance descriptor is converted into sigma-point representation, where scene describing and SVM based training can be completed in Euclidian space. The performance of the novel algorithm is compared with some of classical algorithms u- sing SUN Database. Farther more, the robustness of the algorithm is validated with noise appended data samples. The results show that the proposed algorithm not only has advantages on computation time and classification performance, but also has good robustness to scene noise.