Valueless image removing method based on deep convolutional neural networks

Abstract

The invention relates to a valueless image removing method based on deep convolutional neural networks. The valueless image removing method comprises the steps of firstly, after performing whitening preprocessing on an image sample set, performing pre-training on a sparse autocoder to obtain the initialization results of deep convolutional network parameters, secondly, building a plurality of layers of deep convolutional neural networks and optimizing the network parameters layer by layer, and finally, classifying a plurality of classes of problems by use of a realized multi-classification softmax model and then realizing the removal of valueless images. Due to the automatic image learning characteristic of the sparse autocoder, the classification correction rate of the valueless image removing method based on the deep convolutional neural networks is increased. The plurality of layers of deep convolutional neural networks are built on the basis of the automatic image learning characteristic of the sparse autocoder, the network parameters are optimized layer by layer, the characteristic of each layer after learning is the combination result of the characteristics of the previous layer, and the multi-classification softmax model is trained to judge images, and consequently, the removal of valueless images is realized.

Description

technical field [0001] The invention relates to an aerial image processing method, in particular to a valueless image removal method based on a deep convolutional neural network. Background technique [0002] It is of great significance to automatically and accurately detect valuable targets from aerial videos and remove useless images. The existing valueless image removal methods mainly include: the classification method based on statistical analysis and the method of artificial neural network. The document "From Local Similarity to Global Coding; An Application to Image Classification, CVPR, 2013, p2794-2801" proposes an image classification algorithm that combines local characteristics with global structural information to remove worthless images. This method uses manual selection of the type of local features, and constructs a coding dictionary for all data on such features at the same time; performs pyramid decomposition on the original image, and encodes and pools the...

AI Technical Summary

Problems solved by technology

However, in the stage of manual selection of local feature types, local features must be specified, which cannot fully express the information contained in the image, has limitations and poor noise resistance

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