An Intelligent Image Feature Recognition Algorithm With Hierarchical Attribute Constraints Based on Weak Supervision and Label Correlation

The ability to extract image features largely determines the accuracy of image classification. However, external interferences in images such as translation, rotation, scaling, occlusion, light, and non-linear deformation, result in greater intra-class differences and inter-class similarity, which substantially increases the difficulty of image classification. Benefitting from the excellent ability of feature learning and extraction, Convolutional Neural Networks (CNNs) have achieved good results in the field of image classification. However, they are also characterized by a complex training process and high-demand parameter adjustment. This paper propose a convolutional neural network optimization method to optimize the model parameters with hierarchical attribute constraints and achieve intelligent recognition of specific image features. Based on the optimized model, we further construct weak supervision and label correlation optimization models and provide a visualization solution for feature recognition results. Experimental results demonstrated that the proposed algorithm can efficiently realize intelligent image feature recognition with high accuracy.