K-means and deep learning-based image classification algorithm

Abstract

The invention discloses a K-means and deep learning-based image classification algorithm. The algorithm comprises the steps of 1) taking untagged images as input images, and randomly extracting image blocks to form an untagged image set of the untagged images same in size; 2) extracting a primary optimal clustering center by adopting a K-means algorithm; 3) constructing a feature mapping function, and extracting image features of the untagged image set; 4) performing pooling operation and normalization processing; 5) extracting a secondary optimal clustering center by adopting the K-means algorithm, extracting final image features by adopting convolution operation, and performing standardization processing on the final image features; and 6) classifying the final image features subjected to the standardization processing through a sorter. The algorithm has the advantages of simplicity, high efficiency, few training parameters and the like, and has a very good effect for classification of massive high-dimensional images; and the input images are preprocessed, so that the effects of improving the image classification effect and enhancing the classification precision are achieved.

Description

technical field [0001] The invention belongs to the field of information technology, and specifically refers to an image classification algorithm based on K-means and deep learning, which is suitable for the classification of massive high-dimensional data images in the Internet, and is also used for network image retrieval, video retrieval, Image data classification in remote sensing image classification, interactive games, intelligent robots and other fields. Background technique [0002] In the field of massive image data processing technology, deep learning is a relatively common algorithm. As an algorithm, deep learning was proposed by Hinton in 2006 and has been widely recognized and applied. Its essence is to learn useful and abstract information by establishing an artificial neural network model with multiple hidden layers and large-scale training data. features, the final result is to improve the accuracy of image classification. Therefore, deep learning can well s...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose an image classification algorithm based on K-means and deep learning for the problems of gradient dispersion, insufficient training samples and local optimum in the traditional neural network training method

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