Abstract
Interesting regions are defined as parts of street view scenes that can attract people’s interests when they are moving on the road, and play an important role in various daily-life scenarios. In this paper, therefore, we propose a framework for locating interesting regions in the street and explore the potential use for advanced multimedia applications. Based on the psychological findings and cognitive processes, we proposed and quantified three properties for modeling interesting regions, including attractive, unique, and familiar. Also, a spatial-temporal fusion scheme is developed to combine the multiple properties for discovering the presence of interesting regions. We conduct a set of user studies to demonstrate the effectiveness of our approach. The results support that most users agreed with the interesting regions found by the proposed approach. Finally, a novel application based on interesting regions is also presented for offering an improved navigation experience to vehicle drivers.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Conroy, D.: What’s Your Signage?: How On-Premise Signs Help Small Businesses Tap Into a Hidden Profit Center. New York State Small Business Development Center (2004)
Chi, H.-Y., Cheng, W.-H., Chen, M.-S., Tsui, A.W.: MOSRO: enabling mobile sensing for real-scene objects with grid based structured output learning. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part I. LNCS, vol. 8325, pp. 207–218. Springer, Heidelberg (2014)
Brown, B., Laurier, E.: The normal natural troubles of driving with GPS. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2012)
Survey: Most drivers using GPS say it has led them astray (2013). http://michelinmedia.com/news/survey-drivers-gps-led-astray/
Forlizzi, J., Barley, W.C., Seder, T.: Where should i turn: moving from individual to collaborative navigation strategies to inform the interaction design of future navigation systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2010)
Tsai, T.-H., Cheng, W.-H., You, C.-W., Hu, M.-C., Tsui, A.W., Chi, H.-Y.: Learning and recognition of on-premise signs from weakly labeled street view images. IEEE Transactions on Image Processing (2014)
Elazary, L., Itti, L.: Interesting objects are visually salient. J. Vis. (2008)
Liu, T., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. (2011)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS (2007)
Parikh, D., et al.: Interactively building a discriminative vocabulary of nameable attributes. In: CVPR (2011)
Doersch, C., et al.: What makes paris look like Paris. In: SIGGRAPH (2012)
Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. (2012)
Short Term Memory. http://www.simplypsychology.org/short-term-memory.html (2009)
Huang, Y.-M., et al.: Advances in Multimedia Information Processing. In: PCM (2008)
Ross, T., May, A.: Design advice for the inclusion of landmarks in vehicle navigation systems. Loughborough University (2002)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. (1998)
Hou, X., Harel, J., Koch, C.: Image signature: highlighting sparse salient regions. IEEE Trans. Pattern Anal. Mach. Intell. (2012)
Sivic, J., Zisserman, A.: Efficient visual search of videos cast as text retrieval. IEEE Trans. Pattern Anal. Mach. Intell. (2009)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2010)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: CVPR (2006)
Atkinson, R.C., et al.: The Control Processes of Short-term Memory. Stanford University, Stanford (1971)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Chi, HY., Cheng, WH., You, CW., Chen, MS. (2016). What Catches Your Eyes as You Move Around? On the Discovery of Interesting Regions in the Street. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_64
Download citation
DOI: https://doi.org/10.1007/978-3-319-27671-7_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27670-0
Online ISBN: 978-3-319-27671-7
eBook Packages: Computer ScienceComputer Science (R0)