【摘要】 针对图像表示,提出了一种基于改进稀疏编码模型的图像分类算法.首先,提取表示图像视觉局部特征的SIFT(Scale-Invariant Feature Transform)描述子;然后,利用稀疏编码方法生成基于SIFT描述子的视觉词汇库,将SIFT描述子编成稀疏向量;通过有效稀疏向量的区域融合和空间结合而获取整体的稀疏向量并用于图像表示;最后,采用随机森林多分类器对稀疏向量进行训练和测试.结果表明,与现有的算法相比,该算法的性能更佳,可以有效表示图像的特性并提高其分类的准确率.更多还原

【Abstract】 A novel image categorization algorithm based on improved sparse coding model for image representation and Random Forests for image classification was proposed.Firstly SIFT(Scale-Invariant Feature Transform) descriptors were extracted from images.Then sparse coding was adopted to train a visual dictionary and convert SIFT descriptors into sparse vectors.An efficient pooling method was employed to merge the sparse vectors in each grid of image and a pooled sparse vector was formed to represent the grid.According to the position of grids,the pooled sparse vectors were combined to form a single sparse vector for representing an image.Secondly Random Forests,a multiclass classifier was employed to classify sparse vectors which represent images.The experimental results show that the algorithm outperforms several state-of-the-arts in image categorization.更多还原