Abstract
Sparse representation-based classification (SRC) has been an important approach in pattern classification and widely applied to various fields of visual recognition. However, there are some practical factors to consider in real-world problems, such as pixel damage, block occlusion, illumination, and position change. In recent years, researchers have brought a large number of improvements and proposed various effective algorithms. We improve the degree of sparse representation (SR) based on the probabilistic collaborative representation framework in this paper. Furthermore, a new algorithm is proposed and successfully applied to face recognition. The effectiveness and efficiency of our method are demonstrated experimentally.
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Hu, Y., Zhang, Y. (2020). A Collaborative Sparse Representation-Based Approach for Pattern Classification. In: Kountchev, R., Patnaik, S., Shi, J., Favorskaya, M. (eds) Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Smart Innovation, Systems and Technologies, vol 180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3867-4_20
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DOI: https://doi.org/10.1007/978-981-15-3867-4_20
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