Mass image sorting system based on deep character learning

Abstract

The invention provides a mass image sorting system based on deep character learning. The mass sorting system comprises the following steps that firstly, non-label image data and label image data are input, the non-label image data are pre-processed, interference information is removed, and key information is remained; secondly, K-means character learning is carried out on pre-processed images, and a dictionary of the layer is obtained; thirdly, if the layer is the Nth layer, character mapping is carried out on the dictionary of the layer and the label image data, the fifth step is executed after the deep characters are obtained, or otherwise, character mapping is carried out on the dictionary of the layer and the non-label image data, and the deep characters are obtained; fourthly, the high correlation characters are combined into a receptive field according to the correlation of the deep characters, if the layer is the (N-1)th layer, the fifth step is executed, or otherwise the layer serves as the input information of the next layer and is sent to the second step; fifthly, in the Nth layer, the learned characters are input to an SVM sorter, and sorting is carried out.

Description

field of invention [0001] The invention belongs to the technical field of machine learning and image processing, relates to massive image processing on a distributed platform, and in particular to an implementation scheme of massive image classification based on depth-level features. Background technique [0002] Nowadays, with the development of multimedia technology, a large amount of multimedia data including images, audio, video and other information emerges. How to classify a large amount of information has become a hot issue in the research of multimedia technology. The research task of image classification is mainly composed of preprocessing, Feature extraction and classification are composed of three main links, and each link has an important impact on the classification effect of images. With the rapid development of computer hardware and software and Internet technology, the amount of multimedia data is also increasing at an alarming rate. In the industry, more and...

AI Technical Summary

Problems solved by technology

[0002] Nowadays, with the development of multimedia technology, a large amount of multimedia data including images, audio, video and other information emerges. How to classify a large amount of information has become a hot issue in the research of multimedia technology. The research task of image classification is mainly composed of preprocessing, Feature extraction and classification are composed of three main links, and each link has an important impact on the classification effect of images. With the rapid development of computer hardware and software and Internet technology, the amount of multimedia data is also increasing at an alarming rate. In the industry, more and more information is expressed in the form of images, which undoubtedly brings great challenges to all aspects of the task of image classification.

Traditional image classification is carried out on a single machine by extracting color, texture, and shape features. With the continuous increase of the image library, the complexity continues to increase, and the artificially designed features of the single machine are far from meeting the demand. Using parallel Dealing with is undoubtedly a good solution

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