Abstract
This paper explores a new Local Binary Patterns (LBP) based image descriptor that makes use of the bag-of-words model to significantly improve classification performance for scene images. Specifically, first, a novel multi-neighborhood LBP is introduced for small image patches. Second, this multi-neighborhood LBP is combined with frequency domain smoothing to extract features from an image. Third, the features extracted are used with spatial pyramid matching (SPM) and bag-of-words representation to propose an innovative Bag of Words LBP (BoWL) descriptor. Next, a comparative assessment is done of the proposed BoWL descriptor and the conventional LBP descriptor for scene image classification using a Support Vector Machine (SVM) classifier. Further, the classification performance of the new BoWL descriptor is compared with the performance achieved by other researchers in recent years using some popular methods. Experiments with three fairly challenging publicly available image datasets show that the proposed BoWL descriptor not only yields significantly higher classification performance than LBP, but also generates results better than or at par with some other popular image descriptors.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)
Banerji, S., Sinha, A., Liu, C.: New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing (2013)
Banerji, S., Sinha, A., Liu, C.: Scene image classification: Some novel descriptors. In: IEEE International Conference on Systems, Man, and Cybernetics, Seoul, Korea, October 14-17, pp. 2294–2299 (2012)
Sinha, A., Banerji, S., Liu, C.: Novel color gabor-lbp-phog (glp) descriptors for object and scene image classification. In: The Eighth Indian Conference on Vision, Graphics and Image Processing, Mumbai, India, December 16-19, p. 58 (2012)
Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: Conference on Computer Vision and Pattern Recognition, pp. 524–531 (2005)
Yang, J., Jiang, Y., Hauptmann, A., Ngo, C.: Evaluating bag-of-visual-words representations in scene classification. In: Multimedia Information Retrieval, pp. 197–206 (2007)
Banerji, S., Verma, A., Liu, C.: Novel color LBP descriptors for scene and image texture classification. In: 15th International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, Nevada, July 18-21, pp. 537–543 (2011)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)
Zhu, C., Bichot, C., Chen, L.: Multi-scale color local binary patterns for visual object classes recognition. In: International Conference on Pattern Recognition, Istanbul, Turkey, August 23-26, pp. 3065–3068 (2010)
Gu, J., Liu, C.: Feature local binary patterns with application to eye detection. Neurocomputing (2013)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA (2006)
Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Ninth IEEE International Conference on Computer Vision, pp. 1470–1477 (2003)
Vedaldi, A., Fulkerson, B.: Vlfeat – an open and portable library of computer vision algorithms. In: The 18th Annual ACM International Conference on Multimedia (2010)
Li, L.J., Fei-Fei, L.: What, where and who? classifying event by scene and object recognition. In: IEEE International Conference in Computer Vision (2007)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)
Vapnik, Y.: The Nature of Statistical Learning Theory. Springer (1995)
Li, L.J., Su, H., Xing, E.P., Fei-Fei, L.: Object bank: A high-level image representation for scene classification & semantic feature sparsification. In: Neural Information Processing Systems, Vancouver, Canada (December 2010)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, Singapore, December 4-6, pp. 1794–(1801)
Van Gemert, J., Veenman, C., Smeulders, A., Geusebroek, J.M.: Visual word ambiguity. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(7), 1271–1283 (2010)
Niu, Z., Hua, G., Gao, X., Tian, Q.: Context aware topic model for scene recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 16-21, pp. 2743–2750 (2012)
Bo, L., Ren, X., Fox, D.: Hierarchical matching pursuit for image classification: Architecture and fast algorithms. In: Advances in Neural Information Processing Systems (December 2011)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Banerji, S., Sinha, A., Liu, C. (2013). A New Bag of Words LBP (BoWL) Descriptor for Scene Image Classification. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-40261-6_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
eBook Packages: Computer ScienceComputer Science (R0)