Dataset Information

Visual scenes are categorized by function.

ABSTRACT: How do we know that a kitchen is a kitchen by looking? Traditional models posit that scene categorization is achieved through recognizing necessary and sufficient features and objects, yet there is little consensus about what these may be. However, scene categories should reflect how we use visual information. Therefore, we test the hypothesis that scene categories reflect functions, or the possibilities for actions within a scene. Our approach is to compare human categorization patterns with predictions made by both functions and alternative models. We collected a large-scale scene category distance matrix (5 million trials) by asking observers to simply decide whether 2 images were from the same or different categories. Using the actions from the American Time Use Survey, we mapped actions onto each scene (1.4 million trials). We found a strong relationship between ranked category distance and functional distance (r = .50, or 66% of the maximum possible correlation). The function model outperformed alternative models of object-based distance (r = .33), visual features from a convolutional neural network (r = .39), lexical distance (r = .27), and models of visual features. Using hierarchical linear regression, we found that functions captured 85.5% of overall explained variance, with nearly half of the explained variance captured only by functions, implying that the predictive power of alternative models was because of their shared variance with the function-based model. These results challenge the dominant school of thought that visual features and objects are sufficient for scene categorization, suggesting instead that a scene's category may be determined by the scene's function.

SUBMITTER: Greene MR

PROVIDER: S-EPMC4693295 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Visual scenes are categorized by function.

Greene Michelle R MR Baldassano Christopher C Esteva Andre A Beck Diane M DM Fei-Fei Li L

Journal of experimental psychology. General 20160101 1

How do we know that a kitchen is a kitchen by looking? Traditional models posit that scene categorization is achieved through recognizing necessary and sufficient features and objects, yet there is little consensus about what these may be. However, scene categories should reflect how we use visual information. Therefore, we test the hypothesis that scene categories reflect functions, or the possibilities for actions within a scene. Our approach is to compare human categorization patterns with pr ...[more]

PMID: 26709590

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Project description:Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and the factors that account for their success are unknown. Here we develop a set of techniques for using CNNs to gain insights into the computational mechanisms underlying cortical responses. We focused on responses in the occipital place area (OPA), a scene-selective region of dorsal occipitoparietal cortex. In a previous study, we showed that fMRI activation patterns in the OPA contain information about the navigational affordances of scenes; that is, information about where one can and cannot move within the immediate environment. We hypothesized that this affordance information could be extracted using a set of purely feedforward computations. To test this idea, we examined a deep CNN with a feedforward architecture that had been previously trained for scene classification. We found that responses in the CNN to scene images were highly predictive of fMRI responses in the OPA. Moreover the CNN accounted for the portion of OPA variance relating to the navigational affordances of scenes. The CNN could thus serve as an image-computable candidate model of affordance-related responses in the OPA. We then ran a series of in silico experiments on this model to gain insights into its internal operations. These analyses showed that the computation of affordance-related features relied heavily on visual information at high-spatial frequencies and cardinal orientations, both of which have previously been identified as low-level stimulus preferences of scene-selective visual cortex. These computations also exhibited a strong preference for information in the lower visual field, which is consistent with known retinotopic biases in the OPA. Visualizations of feature selectivity within the CNN suggested that affordance-based responses encoded features that define the layout of the spatial environment, such as boundary-defining junctions and large extended surfaces. Together, these results map the sensory functions of the OPA onto a fully quantitative model that provides insights into its visual computations. More broadly, they advance integrative techniques for understanding visual cortex across multiple level of analysis: from the identification of cortical sensory functions to the modeling of their underlying algorithms.

Dataset Information

Visual scenes are categorized by function.

Publications

Visual scenes are categorized by function.

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