Journal
INFORMATION SCIENCES
Volume 254, Issue -, Pages 141-154Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2013.08.020
Keywords
Architecture style; Block let; Hierarchical; Sparse coding
Categories
Funding
- National Natural Science Foundation of China [61170142]
- National Key Technology R D Program [2011BAG05B04]
- International ScienceAMP
- Technology Cooperation Program of China [2013DFG12840]
- National High Technology Research and Development Program of China [2013AA040601]
- Fundamental Research Funds for the Central Universities
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In this work, we propose a novel architecture style recognition model by introducing blocklets that capture the morphological characteristics of buildings. First, we decompose a building image into a collection of blocks, each representing a basic architecture component such as a stone pillar. To exploit the spatial correlations among blocks, we obtain locklets by extracting spatially adjacent blocks, and further formulate architecture style recognition as matching between blocklets extracted from different buildings. Toward an efficient blocklet-to-blocklet matching, a hierarchical sparse coding algorithm is proposed to represent each blocklet by a linear combination of basis blocklets. On the other hand, toward an effective matching process, an LDA [25,1]-like scheme is adopted to select the blocklets with high discrimination. Finally, we carry out architecture style recognition based on the selected highly discriminative blocklets. Experimental results on our own compiled data set demonstrate that the proposed approach outperforms several state-ofthe-art place/building recognition models. (C) 2013 Elsevier Inc. All rights reserved.
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