Loschky Lester C, Larson Adam M
Department of Psychology, Kansas State University, Manhattan, KS 66506-5302, USA.
J Vis. 2008 Jan 11;8(1):4.1-9. doi: 10.1167/8.1.4.
What information do people use to categorize scenes? Computational scene classification models have proposed that unlocalized amplitude information, the distribution of spatial frequencies and orientations, is useful for categorizing scenes. Previous research has provided conflicting results regarding this claim. Our previous research (Loschky et al., 2007) has shown that randomly localizing amplitude information (i.e., randomizing phase) greatly disrupts scene categorization at the basic level. Conversely, studies suggesting the usefulness of unlocalized amplitude information have used binary distinctions, e.g., Natural/Man-made. We hypothesized that unlocalized amplitude information contributes more to the Natural/Man-made distinction than basic level distinctions. Using an established set of images and categories, we varied phase randomization and measured participants' ability to distinguish Natural versus Man-made scenes or scenes at the basic level. Results showed that eliminating localized information by phase randomization disrupted scene classification even for the Natural/Man-made distinction, demonstrating that amplitude localization is necessary for scene categorization.
人们依据哪些信息对场景进行分类？计算场景分类模型提出，非局部化的幅度信息、空间频率和方向的分布，对于场景分类是有用的。先前的研究对于这一说法给出了相互矛盾的结果。我们之前的研究（Loschky等人，2007年）表明，随机定位幅度信息（即随机化相位）会极大地扰乱基本水平上的场景分类。相反，那些表明非局部化幅度信息有用性的研究使用的是二元区分，例如自然/人造。我们假设，非局部化幅度信息对自然/人造区分的贡献比对基本水平区分的贡献更大。使用一组既定的图像和类别，我们改变相位随机化并测量参与者区分自然场景与人造场景或基本水平场景的能力。结果表明，通过相位随机化消除局部化信息会扰乱场景分类，即使对于自然/人造区分也是如此，这表明幅度定位对于场景分类是必要的。
