A self-stepping image classification method and system

Abstract

The invention discloses a self-stepping enhanced image classification method and system, comprising the following steps: S10: Input image data and its category labels for classification, and extract features from the data; S20: Based on the enhanced learning and self-stepping learning framework, establish Mathematical model; S30: Iteratively update the parameters of the model and the set of weak classifiers of the model until convergence; S40: Predict the category of the newly input test image. The present invention is characterized in that it makes full use of the internal consistency and complementarity of the enhanced learning method and the self-paced learning method, so that the learning process pays attention to the distinguishing ability of the classification model and the reliability of the image samples participating in the learning, and realizes effective learning at the same time. and robust learning. Compared with traditional image classification methods, the present invention has higher classification accuracy and robustness to 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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