Design and Application of an Adaptive Slow Feature Extraction Algorithm for Natural Images Based on Visual Invariance

Received: 3 March 2019 Accepted: 6 May 2019 The invariance of complex visual information in natural images is not considered in existing slow feature (SF) extraction algorithm. To solve the problem, this paper designs an adaptive SF extraction algorithm for natural images based on visual invariance. Firstly, the principal component analysis (PCA) was improved by the topologically independent component analysis (TICA), aiming to adaptively extract the invariance features of complex visual information in natural images. Next, the Markov chain Monte Carlo (MCMC) algorithm and the visual smoothness theory were combined to solve two defects of the conventional slow feature analysis (SFA): the nonlinear expansion algorithm has nothing to do with the visual invariance of the natural images; the sampling algorithm causes the loss of key visual information. Finally, the probability classification was introduced to our algorithm. The experimental results show that our algorithm achieved higher recognition rate and lower computing complexity than conventional algorithms, and exhibited strong robustness and geometric invariance.

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