Significant guidance and unsupervised feature learning based image classification method

Abstract

The invention discloses a significant guidance and unsupervised feature learning based image classification method and belongs to the field of machine learning and computer vision. The image classification method comprises significant guidance based pixel point collection, unsupervised feature learning, image convolution, local comparison normalization, spatial pyramid pooling, central prior fusion and image classification. With the adoption of the classification method, representative pixel points in an image data set are collected through significant detection; the representative pixel points are trained with the sparse self-coding unsupervised feature learning method to obtain high-quality image features; features of a training set and a test set are obtained through image convolution operation; convolution features are subjected to local comparison normalization and spatial pyramid pooling; pooled features are fused with central prior features; and images are classified by adopting a liblinear classifier. According to the method, efficient and robust image features can be obtained and the classification accuracy of various images can be significantly improved.

Description

technical field [0001] The invention relates to an image classification method based on saliency-guided non-supervised feature learning, which belongs to the technical field of machine learning and computer vision. Background technique [0002] With the development of multimedia technology, image classification has become the focus of research in the field of computer vision. Image classification is to divide images into different preset categories according to certain attributes of images. How to effectively express images is the key to improving the The key to the accuracy of image classification, the selection and extraction of features are currently difficult problems in image classification. With the rapid development of the mobile Internet, human society has entered the era of big data. Although traditional feature learning methods such as SIFT and HOG can extract some features of images, they have also achieved good results in image classification, but this artificia...

AI Technical Summary

Problems solved by technology

Although traditional feature learning such as SIFT and HOG can extract some features of images, they have also achieved good results in image classification, but this artificial design feature method has certain defects.

However, the traditional supervised feature learning method, by learning manual labeling data, is outdated in the era of big data.

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