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PCANet-based image classification method and device

A classification method and image technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of slow speed and low precision, and achieve the effect of improving efficiency and precision, high precision, and easy optimization

Pending Publication Date: 2021-05-07
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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AI Technical Summary

Problems solved by technology

In related technologies, traditional image classification algorithms have low precision and slow speed. Using deep learning combined with machine learning classifier classification methods can effectively solve the above problems

Method used

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  • PCANet-based image classification method and device
  • PCANet-based image classification method and device
  • PCANet-based image classification method and device

Examples

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Embodiment Construction

[0112] Embodiments of the present application are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary, and are intended to explain the present application, and should not be construed as limiting the present application.

[0113] In the example of this application, the process of using PCANet model and linear SVM classifier to classify images (Image classification), that is, an image processing method to distinguish different types of objects according to the different features reflected in the image information. It uses computer to carry out quantitative analysis on images, and classifies each pixel or area in the image or image into one of several categories to replace human visual interpretation.

[0114] Before introducing the PCANet-based...

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Abstract

The invention provides a PCANet-based image classification method and device. The method comprises the following steps: acquiring image data; performing data size and format processing on the image data, preprocessing the processed image data, and dividing the preprocessed image data into a training image set and a test image set; performing model training according to the PCANet model and the training image set to generate a PCANet feature extraction model; and inputting the images in the test image set into the trained PCANet feature extraction model to extract features, and inputting the extracted features into an SVM classifier for classification. Images are fully automatically classified based on the PCANet network model, so that the efficiency and effect of the classification process can be improved, the image classification process can be simpler, the complexity of a traditional classification model is effectively reduced, and the classification accuracy and reliability are improved.

Description

technical field [0001] The present application relates to the fields of image processing and pattern recognition, in particular to a PCANet-based image classification method and device. Background technique [0002] The concept of deep learning originates from the research of artificial neural networks. By combining low-level features, a more abstract high-level representation attribute category or feature is formed to discover the distributed feature representation of data. With the rapid development of artificial intelligence in recent years, deep learning has achieved many results in search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning, voice, recommendation and personalization technology, and other related fields . Deep learning enables machines to imitate human activities such as audio-visual and thinking, and solves many complex pattern recognition problems, making great progress in artificial intellig...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/214G06F18/2411
Inventor 王瑜贾洪飞
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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