Abstract
Recently, the use of context has been proven very effective for object categorization. However, most of the researchers only used context information at the visual word level without considering the context information of local features. To tackle this problem, in this paper, we propose a novel object categorization method by considering the local feature context. Given a position in an image, to represent this position’s visual information, we use the local feature on this position as well as other local features based on their distances and angles to this position. The use of local feature context is more discriminative and is also invariant to rotation and scale change. The local feature context can then be combined with the state-of-the-art methods for object categorization. Experimental results on the UIUC-Sports dataset and the Caltech-101 dataset demonstrate the effectiveness of the proposed method.
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Sun, T., Zhang, C., Liu, J., Lu, H. (2013). Object Categorization Using Local Feature Context. In: Li, S., et al. Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol 7733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35728-2_31
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DOI: https://doi.org/10.1007/978-3-642-35728-2_31
Publisher Name: Springer, Berlin, Heidelberg
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