论文标题
场景图像表示前景，背景和混合功能
Scene Image Representation by Foreground, Background and Hybrid Features
论文作者
论文摘要
以前基于深度学习表示场景图像的方法主要将前景信息或背景信息视为分类任务的区分线索。但是，场景图像还需要其他信息（Hybrid）来应对类间的相似性和类内的变化问题。在本文中，我们建议除前景和背景功能外使用混合功能来表示场景图像。我们假设这三种类型的信息可以共同帮助更准确地表示场景图像。为此，我们在ImageNet，Place和Hybrid（ImageNet和Place）数据集上采用了三个VGG-16架构，以相应提取前景，背景和混合信息。所有这三种类型的深度功能都将进一步汇总，以实现我们的最终特征来代表场景图像。在两个大型基准场景数据集（MIT-67和SUN-397）上进行了广泛的实验表明，我们的方法会产生最新的分类性能。
Previous methods for representing scene images based on deep learning primarily consider either the foreground or background information as the discriminating clues for the classification task. However, scene images also require additional information (hybrid) to cope with the inter-class similarity and intra-class variation problems. In this paper, we propose to use hybrid features in addition to foreground and background features to represent scene images. We suppose that these three types of information could jointly help to represent scene image more accurately. To this end, we adopt three VGG-16 architectures pre-trained on ImageNet, Places, and Hybrid (both ImageNet and Places) datasets for the corresponding extraction of foreground, background and hybrid information. All these three types of deep features are further aggregated to achieve our final features for the representation of scene images. Extensive experiments on two large benchmark scene datasets (MIT-67 and SUN-397) show that our method produces the state-of-the-art classification performance.
