Modeling visual clutter perception using proto-object segmentation

Journal of Vision

Volumn 14, Issue 7, 2014, Pages

  Yu, Chen Ping ^a   Samaras, Dimitris ^a   Zelinsky, Gregory J ^a,b  

^a USA   (United States)

^b STONY BROOK UNIVERSITY   (United States)

Author keywords

Color clustering; Image segmentation; Midlevel visual representation; Proto objects; Superpixel merging; Visual clutter

Indexed keywords

ADOLESCENT; ADULT; ARTICLE; ATTENTION; COLOR CLUSTERING; COMPUTER SIMULATION; CROWDING (AREA); EYE MOVEMENT; HUMAN; IMAGE SEGMENTATION; MIDLEVEL VISUAL REPRESENTATION; PHYSIOLOGY; PROTO-OBJECTS; SUPERPIXEL MERGING; VISION; VISUAL CLUTTER; YOUNG ADULT;

COLOR CLUSTERING; IMAGE SEGMENTATION; MIDLEVEL VISUAL REPRESENTATION; PROTO-OBJECTS; SUPERPIXEL MERGING; VISUAL CLUTTER;

ADOLESCENT; ADULT; ATTENTION; COMPUTER SIMULATION; CROWDING; EYE MOVEMENTS; HUMANS; VISUAL PERCEPTION; YOUNG ADULT;

EID: 84902531149     PISSN: None     EISSN: 15347362     Source Type: Journal    
DOI: 10.1167/14.7.4     Document Type: Article

References (57)

1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Susstrunk, S. (2012). SLIC superpixels compared to state-of-the-art superpixel methods. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(11), 2274-2282.
2. Arbelaez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(5), 898-916.
3. Beck, M. R., Lohrenz, M. C., & Trafton, J. G. (2010). Measuring search efficiency in complex visual search tasks: Global and local clutter. Journal of Experimental Psychology: Applied; Journal of Experimental Psychology: Applied, 16(3), 238.
4. Bergen, J. R., & Landy, M. S. (1991). Computational modeling of visual texture segregation. In M. S. Landy & J. A. Movshon, (Eds.), Computational models of visual processing (pp. 253-271). Cambridge, MA: MIT Press.
5. Bravo, M. J., & Farid, H. (2008). A scale invariant measure of clutter. Journal of Vision, 8(1):23, 1-9, http://www.journalofvision.org/content/8/1/23, doi:10.1167/8.1.23. [PubMed] [Article]
6. Bravo, M. J., & Farid, H. (2004). Search for a category target in clutter. Perception, 33, 643-652.
7. Bundesen, C. (1990). A theory of visual attention. Psychological Review, 97(4), 523-547.
8. Burt, P., & Adelson, E. (1983). The Laplacian pyramid as a compact image code. Communications, IEEE Transactions on, 31(4), 532-540.
9. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679-714.
10. Cheng, Y. (1995). Mean shift, mode seeking, and clustering. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 17(8), 790-799.
11. Collins, R. T. (2003). Mean-shift blob tracking through scale space. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Vol. 2, pp. 234-240). Madison, WI: IEEE.
12. Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5), 603-619.
13. Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M., & Reitboeck, H. J. (1988). Coherent oscillations: A mechanism of feature linking in the visual cortex? Biological Cybernetics, 60(2), 121-130.
14. Eckstein, M. P. (2011). Visual search: A retrospective. Journal of Vision, 11(5):14, 1-36, http://www. journalofvision.org/content/11/5/14, doi:10.1167/11.5.14. [PubMed] [Article]
15. Endres, I., & Hoiem, D. (2010). Category independent object proposals. Lecture Notes in Computer Science: Computer Vision-ECCV 2010, 6315, 575-588.
16. Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167-181.
17. Forssén, P. E. (2004). Low and medium level vision using channel representations (Unpublished doctoral dissertation), Linköping University, Linköping, Sweden.
18. Franconeri, S. L., Bemis, D. K., & Alvarez, G. A. (2009). Number estimation relies on a set of segmented objects. Cognition, 113, 1-13.
19. Henderson, J. M., Chanceaux, M., & Smith, T. J. (2009). The influence of clutter on real-world scene search: Evidence from search efficiency and eye movements. Journal of Vision, 9(1):32, 1-8, http://www.journalofvision.org/content/9/1/32, doi:10. 1167/9.1.32. [PubMed] [Article]
20. Hou, X., & Zhang, L. (2007). Saliency detection: A spectral residual approach. In Computer Vision and Pattern Recognition, CVPR'07. IEEE Conference on (pp. 1-8). Minneapolis, MN: IEEE.
21. Itti, L., & Koch, C. (2001). Computational modeling of visual attention. Nature Reviews Neuroscience, 2(3), 194-203.
22. Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(11), 1254-1259.
23. Kappes, J., Speth, M., Andres, B., Reinelt, G., & Schn, C. (2011). Globally optimal image partitioning by multicuts. Lecture Notes in Computer Science: Energy Minimization Methods in Computer Vision and Pattern Recognition, 6819, 31-44.
24. Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79-116.
25. Liu, M. Y., Tuzel, O., Ramalingam, S., & Chellappa, R. (2011). Entropy rate superpixel segmentation. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 2097-2104). Colorado Springs, CO: IEEE.
26. Lohrenz, M. C., Trafton, J. G., Beck, M. R., & Gendron, M. L. (2009). A model of clutter for complex, multivariate geospatial displays. Human Factors, 51(1), 90-101.
27. Mack, M. L., & Oliva, A. (2004). Computational estimation of visual complexity. Poster presented at the Twelfth Annual Object, Perception, Attention, and Memory Conference, Minneapolis, MN.
28. Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In International Conference of Computer Vision (ICCV), IEEE Conference on (pp. 416-423). IEEE.
29. Michailidou, E., Harper, S., & Bechhofer, S. (2008). Visual complexity and aesthetic perception of web pages. In Proceedings of the 26th Annual ACM International Conference on Design of Communication (pp. 215-224). Lisbon, Portugal: ACM.
30. Neider, M. B., & Zelinsky, G. J. (2011). Cutting through the clutter: Searching for targets in evolving complex scenes. Journal of Vision, 11(14): 7, 1-16, http://www.journalofvision.org/content/11/14/7, doi:10.1167/11.14.7. [PubMed] [Article]
31. Neider, M. B., & Zelinsky, G. J. (2008). Exploring set size effects in scenes: Identifying the objects of search. Visual Cognition, 16(1), 1-10.
32. Olshausen, B., Anderson, C., & Van Essen, D. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. Journal of Neuroscience, 13, 4700-4719.
33. Orabona, F., Metta, G., & Sandini, G. (2007). A protoobject based visual attention model. Lecture Notes in Computer Science: Attention in Cognitive Sys-tems. Theories and Systems from an Interdisciplinary Viewpoint, 4840, 198-215.
34. Pauli, H. (1976). Proposed extension of the CIE recommendation on ''Uniform color spaces, color spaces, and color difference equations, and metric color terms''. Journal of the Optical Society of America, 66, 866-867.
35. Pieters, R., Wedel, M., & Zhang, J. (2007). Optimal feature advertising design under competitive clutter. Management Science, 53(11), 1815-1828.
36. Pylyshyn, Z. W. (2001). Visual indexes, preconceptual objects, and situated vision. Cognition, 80(1), 127-158.
37. Rensink, R. A. (2000). The dynamic representation of scenes. Visual Cognition, 7, 17-42.
38. Rensink, R. A. (2010). Seeing seeing. Psyche, 16(1), 68-78.
39. Rensink, R. A., & Enns, J. T. (1998). Early completion of occluded objects. Vision Research, 38, 2489-2505.
40. Rensink, R. A., & Enns, J. T. (1995). Preemption effects in visual search: Evidence for low-level grouping. Psychological Review, 102, 101-130.
41. Rosenholtz, R., Li, Y., Mansfield, J., & Jin, Z. (2005). Feature congestion: A measure of display clutter. In Proceedings of the SIGCHI Conference on Human factors in Computing Systems (pp. 761-770). Portland, OR: ACM.
42. Rosenholtz, R., Li, Y., & Nakano, L. (2007). Measuring visual clutter. Journal of Vision, 7(2):17, 1-22, http://www.journalofvision.org/content/7/2/17, doi:10.1167/7.2.17. [PubMed] [Article]
43. Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8), 888-905.
44. Stone, M. C., Fishkin, K., & Bier, E. A. (1994). The movable filter as a user interface tool. In Proceedings of the SIGCHI Conference on Human factors in Computing Systems: Celebrating Interdependence (pp. 306-312). Boston, MA: ACM.
45. van den Berg, R., Cornelissen, F. W., & Roerdink, J. B. (2009). A crowding model of visual clutter. Journal of Vision, 9(4):24, 1-11, http://www. journalofvision.org/content/9/4/24, doi:10.1167/9. 4.24. [PubMed] [Article]
46. van den Berg, R., Roerdink, J. B., & Cornelissen, F. W. (2007). On the generality of crowding: Visual crowding in size, saturation, and hue compared to orientation. Journal of Vision, 7(2):14, 1-11, http://www.journalofvision.org/content/7/2/14, doi:10. 1167/7.2.14. [PubMed] [Article]
47. van de Sande, K. E., Uijlings, J. R., Gevers, T., & Smeulders, A. W. (2011). Segmentation as selective search for object recognition. In International Conference on Computer Vision (ICCV) (pp. 1879-1886). Barcelona, Spain: IEEE.
48. Veksler, O., Boykov, Y., & Mehrani, P. (2010). Superpixels and supervoxels in an energy optimization framework. Lecture Notes in Computer Science: Computer Vision-ECCV 2010, 6315, 211-224.
49. Walther, D., & Koch, C. (2006). Modeling attention to salient proto-objects. Neural Networks, 19(9), 1395-1407.
50. Wang, J., Jia, Y., Hua, X. S., Zhang, C., & Quan, L. (2008). Normalized tree partitioning for image segmentation. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1-8). Anchorage, AK: IEEE.
51. Whitney, D., & Levi, D. M. (2011). Visual crowding: A fundamental limit on conscious perception and object recognition. Trends in Cognitive Sciences, 15(4), 160-168.
52. Wischnewski, M., Belardinelli, A., Schneider, W. X., & Steil, J. J. (2010). Where to look next? Combining static and dynamic proto-objects in a TVA-based model of visual attention. Cognitive Computation, 2(4), 326-343.
53. Wischnewski, M., Steil, J. J., Kehrer, L., & Schneider, W. X. (2009). Integrating inhomogeneous processing and proto-object formation in a computational model of visual attention. Human Centered Robot Systems, 6, 93-102.
54. Wolfe, J. M. (1998). What can 1 million trials tell us about visual search? Psychological Science, 9, 33-39.
55. Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010). Sun database: Large-scale scene recognition from abbey to zoo. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on (pp. 3485-3492). San Francisco, CA: IEEE.
56. Yang, A. Y., Wright, J., Ma, Y., & Sastry, S. S. (2008). Unsupervised segmentation of natural images via lossy data compression. Computer Vision and Image Understanding, 110(2), 212-225.
57. Yu, Z., Au, O. C., Tang, K., & Xu, C. (2011). Nonparametric density estimation on a graph: Learning framework, fast approximation and application in image segmentation. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 2201-2208). Colorado Springs, CO: IEEE.