Recognizing jumbled images: The role of local and global information in image classification

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 519-526

  Parikh, Devi ^a  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADVANCED MODELING; BAG OF WORDS; COMPUTATIONAL MODEL; COMPUTER VISION ALGORITHMS; FUNCTIONAL MODEL; GLOBAL INFORMATIONS; HUMAN ABILITIES; HUMAN PERFORMANCE; HUMAN STUDY; HUMAN SUBJECTS; LOCAL INFORMATION; PERFORMANCE BOTTLENECKS; RESEARCH EFFORTS;

COMPUTER VISION; IMAGE ANALYSIS; IMAGE CLASSIFICATION;

IMAGE MATCHING;

EID: 84856671912     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126283     Document Type: Conference Paper

References (28)

1. J. Vogel, A. Schwaninger, C. Wallraven and H. H. Blthoff. Categorization of Natural Scenes: Local vs. Global Information. Symposium on Applied Perception in Graphics and Visualization (APGV), 2006.
2. D. Parikh and C. L. Zitnick. The Role of Features, Algorithms and Data in Visual Recognition. CVPR, 2010.
3. L. Fei-Fei and P. Perona. A Bayesian Hierarchical Model for Learning Natural Scene Categories. CVPR, 2005.
4. T. S. Cho, S. Avidan andW. T. Freeman. A Probabilistic Image Jigsaw Puzzle Solver. CVPR, 2010.
5. D. Marr. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman. 1982.
6. D. Parikh and C. L. Zitnick. Finding the Weakest Link in Person Detectors. CVPR, 2011.
7. A. Oliva and A. Torralba. Building the Gist of a Scene: The Role of Global Image Features in Recognition. Progress in Brain Research, 2006.
8. L. FeiFei, R. VanRullen, C. Koch and P. Perona. Rapid natural scene categorization in the near absence of attention. Proc. Nat. Acad. of Sciences, 2002.
9. L. FeiFei, A. Iyer, C. Koch and P. Perona. What do we perceive in a glance of a real-world scene? Journal of Vision, 2007.
10. T. Bachmann. Identification of spatially quantized tachistoscopic images of faces: How many pixels does it take to carry identity? Europ. Jour, of Cognitive Psychology, 1991.
11. A. Oliva and P. G. Schyns. Diagnostic colors mediate scene recognition. Cognitive Psychology, 1976.
12. A. Torralba, R. Fergus and W. T. Freeman. 80 million tiny images: A large dataset for non-parametric object and scene recognition. PAMI, 2008.
13. A. Schwaninger, C. Carbon and H. Leder. Expert Face Processing: Specialization and Constraints. In Development of Face Processing, 2003.
14. W. Hayward. After the Viewpoint Debate: Where Next in Object Recognition? Trends in Cognitive Sciences, 2003.
15. D. Parikh, C. Zitnick and T. Chen. From Appearance to Context-Based Recognition: Dense Labeling in Small Images. CVPR, 2008.
16. A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 2001
17. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. CVPR, 2006.
18. I. Biederman. Perceiving Real-World Scenes. Science, 1972.
19. I. Biederman, A. Glass and E. W. Stacy. Searching for Objects in Real-World Scenes. Journal of Experimental Psychology, 1973.
20. I. Biederman, J. Rabinowitz, A. Glass and E. W. Stacy. On the Information Extracted From a Glance at a Scene. Journal of Experimental Psychology, 1974.
21. G. R. Loftus,W.W. Nelson and H. J. Kallman. Differential Acquisition Rates for Different Types of Information from Pictures. Quarterly Journal of Experimental Psychology, 1983.
22. G. Giraudet and C. Roumes. Independence from Context Information Provided by Spatial Signature Learning in a Natural Object Localization Task. Notes in Artificial Intelligence: Modelling and Using Context, 1999.
23. B. S. Tjan, A. I. Ruppertsberg and H. H. Blthoff. Early use of color but not Local Structure in Rapid Scene Perception. Investigative Ophthalmology and Visual Science, 1999.
24. K. Yokosawa and H. Mitsumatsu. Does Disruption of a Scene Impair Change Detection? Journal of Vision, 2003.
25. A. Quattoni and A. Torralba. Recognizing Indoor Scenes. CVPR, 2009.
26. L. Fei-Fei, R. Fergus and P. Perona. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. CVPR, Workshop on Generative-Model Based Vision, 2004.
27. M. Szummer and R. W. Picard. Indoor-Outdoor Image Classification. IEEE International Workshop on Content-based Access of Image and Video Databases, 1998
28. T. Leung and J. Malik. Representing and Recognizing the Visual Appearance of Materials Using Three-dimensional Textons. IJCV, 2001.