Abstract
Incorporating the spatial information of visual words enhances the performance of the well-known bag-of-visual words (BoVWs) model for problems like object category recognition. However, object images can undergo various in-plane rotations due to which the spatial information must be added to the BoVWs model in rotation-invariant manner. We present a novel approach to integrate the spatial information to BoVWs model in a rotation-invariant way by encoding the triangular relationship among the positions of identical visual words in the \(2D\) image space. Our proposed BoVWs model is based on densely sampled local features for which the dominant orientations are calculated. Thus we achieve rotation-invariance both globally and locally. We validate our proposed method for rotation-invariance on datasets of ancient coins and butterflies and achieve better performance than the conventional BoVWs model.
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Anwar, H., Zambanini, S., Kampel, M.: Supporting ancient coin classification by image-based reverse side symbol recognition. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 17–25. Springer, Heidelberg (2013)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV, pp. 1–22 (2004)
Deselaers, T., Ferrari, V.: Global and efficient self-similarity for object classification and detection. In: CVPR, pp. 1633–1640 (2010)
Kavelar, A., Zambanini, S., Kampel, M., Vondrovec, K., Siegl, K.: The ILAC-project: supporting ancient coin classification by means of image analysis. In: XXIV International CIPA Symposium (2013)
Khan, R., Barat, C., Muselet, D., Ducottet, C.: Spatial orientation of visual word pairs to improve bag-of-visual-words model. In: BMVC, pp. 1–11 (2012)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)
Li, F.F., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR, pp. 524–531 (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)
Penatti, O.A.B., Silva, F.B., Valle, E., Gouet-Brunet, V., da Silva Torres, R.: Visual word spatial arrangement for image retrieval and classification. Pattern Recogn. 47(2), 705–720 (2014)
Perdoch, M., Chum, O., Matas, J.: Efficient representation of local geometry for large scale object retrieval. In: CVPR, pp. 9–16 (2009)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: CVPR (2008)
Veksler, O.: Star shape prior for graph-cut image segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 454–467. Springer, Heidelberg (2008)
Wang, J., Markert, K., Everingham, M.: Learning models for object recognition from natural language descriptions. In: BMVC, pp. 2.1–2.11 (2009)
Zambanini, S., Kampel, M.: Robust automatic segmentation of ancient coins. In: VISAPP, pp. 273–276 (2009)
Zhang, E., Mayo, M.: Enhanced spatial pyramid matching using log-polar-based image subdivision and representation. In: DICTA, pp. 208–213 (2010)
Zhang, J., Marszałek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vision 73(2), 213–238 (2007)
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Anwar, H., Zambanini, S., Kampel, M. (2014). Encoding Spatial Arrangements of Visual Words for Rotation-Invariant Image Classification. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_36
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DOI: https://doi.org/10.1007/978-3-319-11752-2_36
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