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.
Chapter PDF
References
Akselrod-Ballin, A., Ullman, S.: Distinctive and compact features. Image and Vision Computing 26(9), 1269–1276 (2008)
Bourdev, L., Malik, J.: Poselets: body part detectors trained using 3D human pose annotations. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1365–1372 (2009)
Bourdev, L., Maji, S., Brox, T., Malik, J.: Detecting people using mutually consistent poselet activations. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 168–181. Springer, Heidelberg (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 886–893 (2005)
Doersch, C., Singh, S., Gupta, A., Sivic, J., Efros, A.A.: What makes paris look like paris? ACM Transactions on Graphics (SIGGRAPH) 31(4), 101:1–101:9 (2012)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88(2), 303–338 (2010). http://www.pascal-network.org/challenges/VOC/voc2010/workshop/index.html
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. Pattern Analysis and Machine Intelligence (PAMI) 32(9) (2010)
Freiwald, W.A., Tsao, D.Y., Livingstone, M.S.: A Face Feature Space in the Macaque Temporal Lobe. Nature Neuroscience 12(9), 1187–1196 (2009)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Girshick, R.B., Felzenszwalb, P.F., McAllester, D.: Discriminatively trained deformable part models, release 5. http://people.cs.uchicago.edu/~rbg/latent-release5/
Grill-Spector, K., Kushnir, T., Hendler, T.: A sequence of object-processing stages revealed by fMRI in the human occipital lobe. Human Brain Mapping 6(4), 316–328 (1998)
Hsiao, E., Efros, A.A.: DPM superhuman, slides 43–51. http://www.cs.cmu.edu/~efros/courses/LBMV09/presentations/latent_presentation.pdf
Ishai, A., Fairhall, S.L., Pepperell, R.: Perception, memory and aesthetics of indeterminate art. Brain Research Bulletin 73(4–6), 319–324 (2007)
Laporte, P.M.: Cubism and science. The Journal of Aesthetics and Art Criticism 7(3), 243–256 (1949)
Lewis, M.B., Edmonds, A.J.: Face detection: Mapping human performance. Perception 32(8), 903–920 (2003)
Nelson, R.C., Selinger, A.: A cubist approach to object recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 614–621 (1998)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. In: International Conference on Learning Representations (ICLR 2014). CBLS (2014)
Singh, S., Gupta, A., Efros, A.A.: Unsupervised discovery of mid-level discriminative patches. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 73–86. Springer, Heidelberg (2012)
Sinha, P., Torralba, A.: Detecting faces in impoverished images. Journal of Vision 2(7) (2002)
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., Fergus, R.: Intriguing properties of neural networks. CoRR abs/1312.6199 (2013)
Tsao, D.Y., Livingstone, M.S.: Mechanisms of face perception. Annual Review of Neuroscience 31, 411–437 (2008)
Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nature Neuroscience 5(7), 682–687 (2002)
Vogels, R.: Effect of image scrambling on inferior temporal cortical responses. Neuroreport 10(9), 1811–1816 (1999)
Vondrick, C., Khosla, A., Malisiewicz, T., Torralba, A.: HOGgles: visualizing object detection features. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2013)
Wiesmann, M., Ishai, A.: Training facilitates object recognition in cubist paintings. Frontiers in Human Neuroscience 4, 11 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ginosar, S., Haas, D., Brown, T., Malik, J. (2015). Detecting People in Cubist Art. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8925. Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-16178-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16177-8
Online ISBN: 978-3-319-16178-5
eBook Packages: Computer ScienceComputer Science (R0)