Width learning system based on multi-feature extraction

A learning system and multi-feature technology, applied in the field of wide learning system, can solve the problems of less classification accuracy and no model training time
CN111401443AActive Publication Date: 2020-07-10CHONGQING UNIV +1
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING UNIV
Publication Date
2020-07-10

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Abstract

The invention discloses a width learning system based on multi-feature extraction, and the system comprises four sub-width learning systems, wherein each sub-width learning system comprises a featurenode, an enhancement node and a sub-node; each sub-width learning system firstly extracts an image feature from the image data set, combines the image features extracted from the image data set to obtain respective feature nodes, and then enhances the respective feature nodes through an enhanced mapping function to form corresponding enhanced nodes; after each sub-width learning system forms an enhancement node, the feature node of each sub-width learning system is combined with the corresponding enhancement node and then is connected to the sub-node of the sub-width learning system, and thenthe output of the sub-node of each sub-width learning system is normalized and then is connected to the final output layer. The method has the advantages of short model training time and high classification accuracy in the aspect of complex data set classification.
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Description

technical field

[0001] The invention relates to the technical field of image classification, in particular to a width learning system based on multi-feature extraction. Background technique

[0002] Image classification is a hot issue in image processing, which aims to automatically classify a large number of images. This technique is widely used in applications such as pedestrian detection, video analysis, and image quality assessment.

[0003] In recent years, image classification methods based on deep learning have received extensive attention and research. Typical deep learning models include Deep Belief Networks (DBN), Deep Boltzmann Machines (DBM) and Convolutional Neural Networks (CNN). Due to the ability to learn higher-level semantic features, CNN is widely used in image processing, especially image classification. CNN is composed of convolutional layer, pooling layer and fully connected layer, and adopts the method of sharing weights, which can effectively reduc...

Claims

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