Abstract
This paper proposes an improved Gaussian mixture model (GMM) for outdoor scene classification. The GMM is usually solved by the expectation-maximization (EM) algorithm, but the EM algorithm may easily lead to local optima with the convergence speed of this method unstable and different initial values also leading to fluctuations in the algorithm’s performance. As a result, a hybrid particle swarm optimization (PSO) algorithm is introduced to replace the EM algorithm for solving the aforementioned problems with a series of improvements also proposed when the hybrid PSO algorithm is used to solve the GMM. Experimental results with outdoor scene classification show that the proposed algorithm, compared to other algorithms, can enhance global search capabilities and convergence speed; in addition, the accuracy of parameter estimation is high, and classification performance is excellent.
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