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Detecting People in Cubist Art

  • Shiry GinosarEmail author
  • Daniel Haas
  • Timothy Brown
  • Jitendra Malik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)

Abstract

Although the human visual system is surprisingly robust to extreme distortion when recognizing objects, most evaluations of computer object detection methods focus only on robustness to natural form deformations such as people’s pose changes. To determine whether algorithms truly mirror the flexibility of human vision, they must be compared against human vision at its limits. For example, in Cubist abstract art, painted objects are distorted by object fragmentation and part-reorganization, sometimes to the point that human vision often fails to recognize them. In this paper, we evaluate existing object detection methods on these abstract renditions of objects, comparing human annotators to four state-of-the-art object detectors on a corpus of Picasso paintings. Our results demonstrate that while human perception significantly outperforms current methods, human perception and part-based models exhibit a similarly graceful degradation in object detection performance as the objects become increasingly abstract and fragmented, corroborating the theory of part-based object representation in the brain.

Keywords

Object detection Perception Abstract art Cubism 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shiry Ginosar
    • 1
    Email author
  • Daniel Haas
    • 1
  • Timothy Brown
    • 1
  • Jitendra Malik
    • 1
  1. 1.University of California BerkeleyBerkeleyUSA

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