Jongpil Kim
ABSTRACT
Recent studies in image memorability showed that the memorability of an image is a measurable quantity and is closely correlated with semantic attributes. However, the intrinsic characteristics of memorability are not yet fully understood. It has been reported that in contrast to a popular belief unusualness or aesthetic beauty of the image may not be positively correlated with the image memorability. This counter-intuitive characteristic of memorability hinders a better understanding of image memorability and its applicability. In this paper, we investigate two new spatial features that are closely correlated with the image memorability yet intuitively explainable. We propose the Weighted Object Area (WOA) that jointly considers the location and size of objects and the Relative Area Rank (RAR) that captures the relative unusualness of the size of objects. We empirically demonstrate their useful correlation with the image memorability. Results show that both WOA and RAR can improve the memorability prediction. In addition, we provide evidence that the RAR can effectively capture object-centric unusualness of size.
- A. C. Berg, T. L. Berg, H. Daumé III, J. Dodge, A. Goyal, X. Han, A. Mensch, M. Mitchell, A. Sood, K. Stratos, and K. Yamaguchi. Understanding and Predicting Importance in Images. In CVPR, IEEE Intl. Conf. on, 2012. Google Scholar
Digital Library
- T. F. Brady, T. Konkle, G. A. Alvarez, and A. Oliva. Visual long-term memory has a massive storage capacity for object details. PNAS, 105(38):14325--14329, September 2008.Google ScholarCross Ref
- C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans. on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Google ScholarDigital Library
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, IEEE Intl. Conf. on, volume 1, pages 886 --893 vol. 1, june 2005. Google ScholarDigital Library
- S. Dhar, V. Ordonez, and T. L. Berg. High Level Describable Attributes for Predicting Aesthetics and Interestingness. In CVPR, IEEE Intl. Conf. on, 2010. Google ScholarDigital Library
- P.F. Felzenszwalb, R.B. Girshick, D. McAllester, and D. Ramanan. Object Detection with Discriminatively Trained Part-Based Models. IEEE Trans. Pattern Anal. Mach. Intell., 32(9):1627 --1645, sept. 2010. Google ScholarDigital Library
- S. J. Hwang and K. Grauman. Reading between the Lines: Object Localization Using Implicit Cues from Image Tags. IEEE Trans. Pattern Anal. Mach. Intell., 34(6):1145--1158, June 2012. Google ScholarDigital Library
- P. Isola, D. Parikh, A. Torralba, and A. Oliva. Understanding the Intrinsic Memorability of Images. In NIPS 24, 2011.Google ScholarCross Ref
- P. Isola, J. Xiao, A. Torralba, and A. Oliva. What makes an image memorable? In CVPR, IEEE Intl. Conf. on, 2011. Google ScholarDigital Library
- A. Khosla, J. Xiao, A. Torralba, and A. Oliva. Memorability of Image Regions. In NIPS 25, 2012.Google Scholar
- T. Konkle, T. F. Brady, G. A. Alvarez, , and A. Oliva. Scene Memory Is More Detailed Than You Think: The Role of Categories in Visual Long-Term Memory. Psychological Science, 21(11):1551--1556, 2010.Google ScholarCross Ref
- S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, IEEE Intl. Conf. on, volume 2, pages 2169 -- 2178, 2006. Google ScholarDigital Library
- A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vision, 42(3):145--175, May 2001. Google ScholarDigital Library
- E. Shechtman and M. Irani. Matching local self-similarities across images and videos. In CVPR, IEEE Intl. Conf. on, pages 1--8, june 2007.Google Scholar
- J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. SUN Database: Large-scale Scene Recognition from Abbey to Zoo. In CVPR, IEEE Intl. Conf. on, 2010.Google ScholarCross Ref
Index Terms
- Relative spatial features for image memorability
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