Abstract
Classifying scenes (such as mountains, forests) is not an easy task owing to their variability, ambiguity, and the wide range of illumination and scale conditions that may apply. Bag of features (BoF) model have achieved impressive performances in many famous databases (such as the 15 scene dataset). A main drawback of the BoF model is it disregards all information about the spatial layout of the features, leads to a limited descriptive ability. In this paper, we use co-occurrence matrix to implant the spatial relations between local features, and demonstrate that feature co-occurrence matrix (FCM) is a potential discriminative character to scenes classification. We propose three FCM based image representations for scenes classification. The experimental results show that, under equal protocol, the proposed method outperforms BoF model and Spatial Pyramid (SP) model and achieves a comparable performance to the state-of-the-art.
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Acknowledgement
This work was supported in part by Beijing Higher Education Young Elite Teacher Project under Grants YETP0514, and NSFC under Grants 61471024. Ling was supported in part by the US NSF Grants IIS-1218156 and IIS-1350521.
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Lang, H., Xi, Y., Hu, J., Du, L., Ling, H. (2015). Scene Classification by Feature Co-occurrence Matrix. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_36
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DOI: https://doi.org/10.1007/978-3-319-16628-5_36
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