Concave-convex local binary features for automatic target recognition in infrared imagery
Date
2014-04-21Author
Sun, Junding
Fan, Guoliang
Yu, Liangjiang
Wu, Xiaosheng
Metadata
Show full item recordAbstract
This paper presents a novel feature extraction algorithm based on the local binary features for automatic target recognition (ATR) in infrared imagery. Since the inception of the local binary pattern (LBP) and local ternary pattern (LTP) features, many extensions have been proposed to improve their robustness and performance in a variety of applications. However, most attentions were paid to improve local feature extraction with little consideration on the incorporation of global or regional information. In this work, we propose a new concave-convex partition (CCP) strategy to improve LBP and LTP by dividing local features into two distinct groups, i.e., concave and convex, according to the contrast between local and global intensities. Then two separate histograms built from the two categories are concatenated together to form a new LBP/LTP code that is expected to better reflect both global and local information. Experimental results on standard texture images demonstrate the improved discriminability of the proposed features and those on infrared imagery further show that the proposed features can achieve competitive ATR results compared with state-of-the-art methods.
Citation
Sun, J., Fan, G., Yu, L., & Wu, X. (2014). Concave-convex local binary features for automatic target recognition in infrared imagery. EURASIP Journal on Image and Video Processing, 2014, Article 23. https://doi.org/10.1186/1687-5281-2014-23