Abstract
In this article, we propose a novel multi-task hybrid dictionary learning approach for moving vehicle classification tasks using multi-sensor networks to improve the classification accuracy in complex scenes with low time complexity, which considers both correlations and complementary information among multiple heterogeneous sensors simultaneously to learn a hybrid dictionary within observations of each sensor. The efficient hybrid dictionary consists of a synthesis dictionary and an analysis dictionary, where discriminative codes can be generated by the trained analysis dictionary and class-specific discriminative reconstruction can be achieved by the trained synthesis dictionary. Extensive experiments are conducted on real data sets captured by the multiple heterogeneous sensors, and the results demonstrate that the proposed method can use the multi-feature fusion method to improve the vehicle classification accuracy, and it can learn a hybrid dictionary to make sure that the sparse coding matrix is obtained by simple linear mapping function. Moreover, the problem of [Formula: see text]-norm[Formula: see text] sparse coding can been solved, to reduce the time complexity of this algorithm, compared with support vector machine, sparse representation classification, label consistent KSVD, Fisher discrimination dictionary learning, hybrid dictionary learning, multi-task sparse representation classification, and multi-task Fisher discrimination dictionary learning algorithms.
Highlights
Multi-sensor fusion has attracted a wide range of attentions over the past few years for both civilian and military applications.[1,2,3] Among them, classification acted as a particular interest in multi-sensor fusion, especially for the moving vehicle classification,[4,5] in which the essential problem is how to make use of relevant information from different tasks while recording the same physical events to achieve an improvement in the classification performance
Many classification approaches have been put forward to improve the applicability for different situations and make the classification performance enhanced, such as support vector machines (SVM),[8,9] sparse representation classification (SRC),[10,11,12] Kernel sparse representation classification (KSRC),[13,14] label consistent KSVD (LC-KSVD),[15,16] Fisher discrimination dictionary learning (FDDL),[17,18] and hybrid dictionary learning (HDL).[19]
To improve the classification performance considerably, Guo et al.[19] proposed a HDL for vehicle classification in acoustic sensor networks, in which discriminative codes can be generated by the trained analysis dictionary and class-specific discriminative reconstruction can be achieved by the trained synthesis dictionary
Summary
Multi-sensor fusion has attracted a wide range of attentions over the past few years for both civilian and military applications.[1,2,3] Among them, classification acted as a particular interest in multi-sensor fusion, especially for the moving vehicle classification,[4,5] in which the essential problem is how to make use of relevant information from different tasks while recording the same physical events to achieve an improvement in the classification performance. To improve the classification performance considerably, Guo et al.[19] proposed a HDL for vehicle classification in acoustic sensor networks, in which discriminative codes can be generated by the trained analysis dictionary and class-specific discriminative reconstruction can be achieved by the trained synthesis dictionary It only considers the classification problem of a single sensor, paying close attention to classification the types of the moving vehicles to achieve the improvement of the classification accuracy with low-time complexity under complex scenes. Extensive experiments are shown in section ‘‘Experimental results’’ and conclusions are drawn in section ‘‘Conclusion.’’
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