Abstract
Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions.
Highlights
Automatic recognition of targets in infrared images is a challenging problem because of some inherent characteristics of the infrared image itself
By adding a spatial local manifold constraint into the classical sparse coding algorithm, we propose the local structure preserving sparse coding (LSPSc) and kernel LSPSc (K-LSPSc) formulation in this paper, which can simultaneously preserve both the sparsity of patches and the intrinsic structure of subregions
We analyze the relationships between LSPSc/K-LSPSc, Sc, kernel sparse coding (KSc), laplacian sparse coding (LSc) and structured sparse coding
Summary
Automatic recognition of targets in infrared images is a challenging problem because of some inherent characteristics of the infrared image itself. Infrared images have the vignetting effect and smooth texture, contain different level of noise and pixel mixing. Infrared targets have inconsistent brightness, which is related to target orientation, surface material, etc. Non-rigid targets have diverse postures, shapes, and sizes, such as humans and animals [2]. Combined with the imaging angles, scene clutters, background occlusions, and other factors [2,3,4], these could all be the constraints of infrared target recognition. Achieving robust target recognition with anti-interference ability (noise, fuzzification, occlusion, target shape, and scene changes) from an infrared image is still a challenging work
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