Self-paced reinforcement image classification method and system

Abstract

The invention discloses a self-paced reinforcement image classification method and system. The method comprises the following steps: S10, inputting image data for classification and type labels, and performing feature extraction on the data; S20, based on reinforcement learning and self-paced learning frameworks, establishing a mathematic model; S30, updating parameters of the model and a weak classifier set of the model in an iteration mode until convergence is realized; and S40, predicting types of newly input test images. The method is characterized in that intrinsic consistency and complementarity of a reinforcement learning method and a elf-paced learning method are fully utilized, a distinguishing capability of a classification model and the reliability of image samples participating in learning are simultaneously highlighted in a learning process, and at the same time, effective learning and robust learning are realized. Compared to a conventional image classification method, the method and system have higher classification accuracy and higher robustness for label noise.

Description

technical field [0001] The invention relates to the fields of image classification, self-paced learning and enhanced learning, in particular to an image classification method and system based on self-paced enhanced learning. Background technique [0002] With the popularity of networks and cameras, various forms of image data are growing explosively, and classification machine learning techniques that understand image content, mine meaningful patterns, and make accurate category predictions from massive image data are particularly important. In general, two basic principles of machine learning are the effectiveness of learning and the robustness (robustness) of learning. On the one hand, the distribution of image data features has high complexity and nonlinearity; in this regard, effective learning requires that the learned model should accurately reflect the intrinsic distribution pattern of the data to achieve accurate prediction. On the other hand, the sources of image d...

AI Technical Summary

Problems solved by technology

However, since each step of reinforcement learning is aimed at the current wrongly predicted samples, it will be sensitive to noisy samples and samples with complex patterns, and the optimization process of fitting the model to these samples will reduce the generalization ability of the model and thus reduce the predictive performance

Therefore, the reinforcement learning model has high discriminative power and effectiveness, but lacks the robustness of learning

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