Estimating scene typicality from human ratings and image features

Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Volumn , Issue , 2011, Pages 2562-2567

  Ehinger, Krista A ^a   Xiao, Jianxiong ^b   Torralba, Antonio ^b   Oliva, Aude ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

categorization; prototypes; scene perception

Indexed keywords

AMAZON'S MECHANICAL TURKS; CATEGORIZATION; COMPUTATIONAL MODELLING; IMAGE FEATURES; ONLINE RATINGS; PROTOTYPE; SCENE CATEGORIES; SCENE CLASSIFICATION; SCENE PERCEPTION; VISUAL ENTITIES;

EID: 84871731176     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Biederman, I. (1987). Recognition-by-Components: A Theory of Human Image Understanding. Psychological Review, 94, 115-147.
2. Blanz, V., Tarr, M. J., & Bülthoff, H. H. (1999). What object attributes determine canonical views? Perception, 28, 575-599.
3. Dalal, N., & Triggs, B. (2005). Histogram of oriented gradient object detection. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 886-893.
4. Epstein, R. & Kanwisher, N. (1998) A cortical representation of the local visual environment. Nature, 392, 598-60.
5. Felzenszwalb, P., Girshick, R., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part based models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 32, 1627-1645.
6. Fei-Fei, L., & Perona, P. (2005). A bayesian hierarchical model for learning natural scene categories. In Proceedings of Computer Vision and Pattern Recognition, 524-531.
7. Greene, M.R., & Oliva, A. (2009). Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cognitive Psychology, 58, 137-179.
8. Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of Computer Vision and Pattern Recognition, 2169-2178.
9. Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal in Computer Vision, 42, 145-175.
10. Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353-363.
11. Renninger, L.W., & Malik, J. (2004). When is scene recognition just texture recognition? Vision Research, 44, 2301-2311.
12. Rosch, E. (1973). Natural categories. Cognitive Psychology, 4, 328-350.
13. Rosch, E., Mervis, C., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382-439.
14. Ross, M.G., & Oliva, A. (2010). Estimating perception of scene layout properties from global image features. Journal of Vision, 10, 1-25.
15. Shechtman, E., & Irani, M. (2007). Matching local self-similarities across images and videos. In Proc. IEEE Conf. Computer Vision and Pattern Recognition.
16. Tversky, B., & Hemenway, K. (1983). Categories of environmental scenes. Cognitive Psychology, 15, 121-149.
17. Vogel, J., & Schiele, B. (2004). A semantic typicality measure for natural scene categorization. In Pattern Recognition Symposium DAGM 2004.
18. Vogel, J., & Schiele, B. (2007). Semantic model of natural scenes for content-based image retrieval. International Journal of Computer Vision, 72, 133-157.
19. Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010). SUN Database: Large scale scene recognition from abbey to zoo. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition.