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Second-Order Statistical Modeling Method of Depth Features in Image Classification

Second-order statistical modeling method of depth features in image classification is studied in this paper. Through the confrontation training of feature generators and classifiers, this encourages the generator to output the features with category information, which improves the generalization ability of the model to a certain extent. Spatial relationship modeling, as an important means to realize image semantic expression can effectively enhance the performance of visual feature expression, the statistical modeling is then applied to the initial model to obtain the better complexity of the framework. The proposed method will be tested on the public data and also the robustness is tested.