School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia.
Sensors (Basel). 2019 Nov 19;19(22):5051. doi: 10.3390/s19225051.
Image set matching (ISM) has attracted increasing attention in the field of computer vision and pattern recognition. Some studies attempt to model query and gallery sets under a joint or collaborative representation framework, achieving impressive performance. However, existing models consider only the competition and collaboration among gallery sets, neglecting the inter-instance relationships within the query set which are also regarded as one important clue for ISM. In this paper, inter-instance relationships within the query set are explored for robust image set matching. Specifically, we propose to represent the query set instances jointly via a combined dictionary learned from the gallery sets. To explore the commonality and variations within the query set simultaneously to benefit the matching, both low rank and class-level sparsity constraints are imposed on the representation coefficients. Then, to deal with nonlinear data in real scenarios, the'kernelized version is also proposed. Moreover, to tackle the gross corruptions mixed in the query set, the proposed model is extended for robust ISM. The optimization problems are solved efficiently by employing singular value thresholding and block soft thresholding operators in an alternating direction manner. Experiments on five public datasets demonstrate the effectiveness of the proposed method, comparing favorably with state-of-the-art methods.
图像集匹配（ISM）在计算机视觉和模式识别领域引起了越来越多的关注。一些研究试图在联合或协作表示框架下对查询集和图库集进行建模，取得了令人印象深刻的性能。然而，现有的模型只考虑了图库集之间的竞争和协作，忽略了查询集中实例之间的关系，这些关系也被认为是 ISM 的一个重要线索。在本文中，我们探索了查询集中实例之间的关系，以实现稳健的图像集匹配。具体来说，我们通过从图库集中学习的组合字典来共同表示查询集实例。为了同时探索查询集中的共性和变化，以有利于匹配，对表示系数施加了低秩和类级稀疏约束。然后，为了处理实际场景中的非线性数据，还提出了核化版本。此外，为了解决查询集中混合的严重损坏，还扩展了所提出的模型以进行稳健的 ISM。通过交替方向方式使用奇异值阈值和块软阈值操作符，可以有效地解决优化问题。在五个公共数据集上的实验表明了所提出方法的有效性，与最先进的方法相比具有优势。
