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Mining location-aware discriminative blocklets for recognizing landmark architectures

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Abstract

Building recognition is an important and still challenging problem in computer vision and pattern recognition field. In this work, we propose a location-aware building recognition model by introducing blocklets, i.e., spatial adjacent blocks associated with their relative positions. First, by evenly partitioning each building image into blocks, we construct a spatial pyramid to describe the spatial relations of blocks. Second, we obtain blocklets by extracting spatial adjacent blocks. And we can cast the building recognition as matching between blocklets from different buildings. Third, towards an efficient matching, a hierarchical sparse coding method is proposed to represent each blocklet by a linear combination of basis blocklets. Furthermore, towards an effective matching, an LDA Jieping (In: Proceedings of ICML 1087–1093 2007)-like scheme is adopted to select the blocklets with high discrimination. Finally, we carry out the architecture recognition based on the selected highly discriminative blocklets. Experimental results on four datasets demonstrate our model is robust to occlusions and large change in backgrounds.

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Notes

  1. The data set will be available online when the paper get accepted.

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Authors and Affiliations

  1. Department of Computer Science, Zhejiang University, Hangzhou, China

    Yi Li & Sanyuan Zhang

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Luming Zhang

Authors
  1. Yi Li
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  2. Sanyuan Zhang
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  3. Luming Zhang
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Correspondence to Luming Zhang.

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Li, Y., Zhang, S. & Zhang, L. Mining location-aware discriminative blocklets for recognizing landmark architectures. Multimedia Systems 22, 455–464 (2016). https://doi.org/10.1007/s00530-014-0409-6

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  • DOI: https://doi.org/10.1007/s00530-014-0409-6

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