Scene classification aims at grouping images into semantic categories. In this article, a new scene classification method is proposed. It consists of regularized auto-encoder-based feature learning step and SVM-based classification step. In the first step, the regularized auto-encoder, imposed with the maximum scatter difference (MSD) criterion and sparse constraint, is trained to extract features of the source images. In the second step, a multi-class SVM classifier is employed to classify those features. To evaluate the proposed approach, experiments based on 8-category sport events (LF data set) are conducted. Results prove that the introduced approach significantly improves the performance of the current popular scene classification methods.
CITATION STYLE
Li, Y., Li, N., Yin, H., Chai, Y., & Jiao, X. (2016). Scene classification based on regularized auto-encoder and SVM. In Lecture Notes in Electrical Engineering (Vol. 360, pp. 85–93). Springer Verlag. https://doi.org/10.1007/978-3-662-48365-7_9
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