Abstract
More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR‐10 data set, which is much higher than the traditional CNN algorithm.
Highlights
The convolutional neural network can read attributes
Method of image classification algorithm (ICA) Based on Multilabel Learning of Improved Convolutional Neural Network Based on Big Data
Experiment on ICA Based on Multilabel Learning of Improved Convolutional Neural Network Based on Big Data
Summary
The convolutional neural network can read attributes. The convolutional neural network should be useful for Internet learning. Cao et al believe that data collection is an important way to reduce the power fee of networks (WSN) He combined the theory of the original set with the improved convergence neural network and proposed a new wireless sensor network information collection algorithm. Traditional machine learning algorithms need a feature extraction process to collect useful information from initial data samples to obtain a set of feature vectors and use a trained classifier for final classification and recognition [14]. ICA theory believes that the mixed data matrix X used for observation is obtained by linear weighting of the independent element S through A
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