Abstract
In this paper, we try to address the challenging problem of combining local shape features to describe long and continuous shape characteristics. To this end, we firstly propose a novel type of local shape feature, namely Active Contour Fragment (ACF), to encode the shape deformation in a local region. An ACF is automatically learnt from the contours of a specific object class and capable to describe the intra-class shape characteristics based on the point distribution model. Secondly, we combine multiple ACFs into a group, namely Active Contour Group (ACG), to describe the long shape characteristics .We model the ACFs in an ACG using an undirected chain model and estimate the parameters of the chain model in a subspace for accelerating the learning and matching processes of ACGs. Finally, we discriminatively train the classifiers based on ACFs and ACGs in a boosting framework for localizing objects as well as delineating object boundaries. Both qualitative and quantitative evaluations show that our approach is capable of describing long shapes and the proposed recognition algorithm achieves promising performance on the public datasets.
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Zheng, W., Song, S., Chang, H., Chen, X. (2013). Grouping Active Contour Fragments for Object Recognition. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_22
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DOI: https://doi.org/10.1007/978-3-642-37331-2_22
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