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A conceptual framework of computations in mid-level vision

  • Kubilius, Jonas1, 2
  • Wagemans, Johan2
  • Op de Beeck, Hans P.1
  • 1 Laboratory of Biological Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
  • 2 Laboratory of Experimental Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
Published Article
Frontiers in Computational Neuroscience
Frontiers Media SA
Publication Date
Dec 12, 2014
DOI: 10.3389/fncom.2014.00158
  • Neuroscience
  • Hypothesis and Theory Article


If a picture is worth a thousand words, as an English idiom goes, what should those words—or, rather, descriptors—capture? What format of image representation would be sufficiently rich if we were to reconstruct the essence of images from their descriptors? In this paper, we set out to develop a conceptual framework that would be: (i) biologically plausible in order to provide a better mechanistic understanding of our visual system; (ii) sufficiently robust to apply in practice on realistic images; and (iii) able to tap into underlying structure of our visual world. We bring forward three key ideas. First, we argue that surface-based representations are constructed based on feature inference from the input in the intermediate processing layers of the visual system. Such representations are computed in a largely pre-semantic (prior to categorization) and pre-attentive manner using multiple cues (orientation, color, polarity, variation in orientation, and so on), and explicitly retain configural relations between features. The constructed surfaces may be partially overlapping to compensate for occlusions and are ordered in depth (figure-ground organization). Second, we propose that such intermediate representations could be formed by a hierarchical computation of similarity between features in local image patches and pooling of highly-similar units, and reestimated via recurrent loops according to the task demands. Finally, we suggest to use datasets composed of realistically rendered artificial objects and surfaces in order to better understand a model's behavior and its limitations.

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