Fast Image Categorization Method using Scene-Context Scale Information

我们提出了缩放优化的文本来学习场景中每个对象的最佳缩放。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一个可视单词的规模优化码本。我们使用每个图像中的场景-上下文尺度来解决文本的尺度优化问题，该尺度是局部上下文在场景中对图像像素进行分类的有效尺度。我们使用随机决策森林来执行文本化过程，随机决策森林是视觉应用中计算效率高的强大工具。我们使用MSRC21和VOC 2007分割数据集的实验结果表明，我们的尺度优化文本提高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.