| Summary: | Most of existing classification methods for image sets are costly,having high computational complexity and poor timeliness.To address the problem,this paper proposes an improved image reconstruction and recognition algorithm.The algorithm uses the Linear Regression Classification(LRC) and Share Nearest Neighbor(SNN) subspace classification theory for image reconstruction and classification.The high-dimensional space built by image subsampling is taken as subspace to avoid the training process with high computational complexity.Then,subspace of different categories of image sets is used to implement regression model estimation for test images.For images in the test set of regression model reconstruction,their categories are determined by using the weighted voting strategy to estimate the test set under the principle that the errors between reconstructed images and original images should be minimized.Experimental results on UCSD/Honda,CMU,ETH-8 and YouTube datasets show that under low-resolution sampling conditions,compared with the ADNT algorithm,the proposed algorithm increases the average classification accuracy by 3.6%,computational efficiency by 10 times,and shortens the fastest response time to 2.8 ms.
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