Fast Image Categorization Method using Scene-Context Scale Information

我们提出了比例优化的文本节点来学习场景中每个对象的最佳比例。我们将它们结合到图像分类和语义分割中。我们的文本化模块生成了一个比例优化的视觉单词码本。我们利用每幅图像中的场景-上下文尺度来研究文本的尺度优化问题，该尺度是对场景中的图像像素进行分类的局部上下文的有效尺度。我们使用随机决策森林来执行文本化过程，这是一个在视觉应用中具有很高计算效率的强大工具。使用MSRC21和VOC 2007分割数据集的实验结果表明，我们的比例优化文本改善了图像分类和分割性能。

We propose scale-optimized textons to learn the best scale for each object in a scene. We incorporate them into image categorization and semantic segmentation. Our textonization module produces a scale-optimized codebook of visual words. We approach the scale-optimization problem of textons using the scene-context scale in each image, which is the effective scale of local context to classify an image pixel in a scene. We perform the textonization process using a randomized decision forest, which is a powerful tool with high computational efficiency in vision applications. Results of our experiments using MSRC21 and VOC 2007 segmentation datasets demonstrate that our scale-optimized textons improve image categorization and segmentation performance.