Abstract
Abstract Abstract 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.
Highlights
Automatic target recognition (ATR) is an important and challenging problem for a wide range of military and civilian applications
It is clear that concave-convex partition (CCP) can improve the local binary pattern (LBP), local ternary pattern (LTP), soft LBP (SLBP), and complete LBP (CLBP) greatly
CCLBPrPi,uR2, CCLTPrPi,uR2, CCSLBPrPi,uR2 and CCCLBP_SPri,uR2/MPri,uR2 get an averaged accuracy improvement of 8.1%, 3.6%, 5.1%, and 1.6% over their original versions, respectively
Summary
Automatic target recognition (ATR) is an important and challenging problem for a wide range of military and civilian applications. Many ATR algorithms have been proposed for forward-looking infrared (FLIR) imagery which can be roughly classified into two groups, i.e., learning-based and model-based [1]. A classifier or a subspace representation which is learned for a set of labeled training data is used for target recognition and classification [2,3]. The model-based approaches involve a set of target templates or feature maps created from CAD models or a model database and match them with observed features to fulfill the ATR task [4,5,6,7,8]. In [9], Patel et al introduced an interesting ATR algorithm that was motivated by sparse representation-based classification (SRC) [10], which outperforms the traditional ones with promising results. There are many hybrid vision approaches that combine learning-based and model-based ideas for object tracking and recognition in visible-band images [11,12,13]
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