Abstract
In this paper we study different approaches that can be used in recognizing landscape scenes. The primary goal has been to find an accurate but still computationally light solution capable of real-time operation. Recognizing landscape images can be thought of a special case of scene classification. Even though there exist a number of different approaches concerning scene classification, there are no other previous works that try to classify images into such high level categories as landscape and non-landscape. This study shows that a global texture-based approach outperforms other more complex methods in the landscape image recognition problem. Furthermore, the results obtained indicate that the computational cost of the method relying on Local Binary Pattern representation is low enough for real-time systems.
Chapter PDF
Similar content being viewed by others
References
Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Improving color constancy using indoor–outdoor image classification. IEEE TIP 17(12), 2381–2392 (2008)
Bosch, A., Munoz, X., Marti, R.: Which is the best way to organize/classify images by content? Image and Vision Computing 25(6), 778–791 (2007)
Chung, D., Kim, S., Bae, J., Lee, S.: Photographic expert-like capturing by analyzing scenes with representative image set. In: Casasent, D.P., Hall, E.L., Röning, J. (eds.) Proc. SPIE, vol. 7252 (2009)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proc. Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, p. 22 (2004)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 5:1–5:60 (2008)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results (2007), http://www.pascal-network.org/challenges/VOC/voc2007/workshop/
Flickr: Flickr homepage (2010), http://www.flickr.com/search/?q=landscape
Kim, W., Park, J., Kim, C.: A novel method for efficient indoor-outdoor image classification. Journal of Signal Processing Systems 61, 251–258 (2010)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. IEEE CVPR, vol. 2, pp. 2169–2178 (2006)
Lipowezky, U., Vol, I.: Indoor-outdoor detector for mobile phone cameras using gentle boosting. In: Proc. IEEE CVPR Workshops (CVPRW). pp. 31–38 (2010)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV 42(3), 145–175 (2001)
Payne, A., Singh, S.: Indoor vs. outdoor scene classification in digital photographs. Pattern Recognition 38(10), 1533–1545 (2005)
Serrano, N., Savakis, A., Luo, A.: A computationally efficient approach to indoor/outdoor scene classification. In: Proc. IEEE ICPR, vol. 4, pp. 146–149 (2002)
Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proc. IEEE Workshop on Content-Based Access of Image and Video Database, pp. 42–51 (1998)
Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Zhang, H.J.: Image classification for content-based indexing. IEEE TIP 10(1), 117–130 (2001)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE TPAMI 32(9), 1582–1596 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huttunen, S., Rahtu, E., Kunttu, I., Gren, J., Heikkilä, J. (2011). Real-Time Detection of Landscape Scenes. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-21227-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21226-0
Online ISBN: 978-3-642-21227-7
eBook Packages: Computer ScienceComputer Science (R0)