Natural image classification method and device on basis of multi-modal matrix filling

Abstract

The invention relates to a natural image classification method and device on basis of multi-modal matrix filling. The method comprises the steps of carrying out feature extraction on natural image data with labels, natural image data without labels and natural image data for testing, and obtaining different feature representations; adopting a matrix filling algorithm to generate estimation labels of all features of the data with the labels; carrying out linear combination on all the estimation labels to be approximate to the corresponding known true labels corresponding to the estimation labels to obtain a combination coefficient; for all features, utilizing the natural image data with the labels and adopting the matrix filling algorithm to predict the labels of the natural image data without the labels and the labels of the natural image data for testing; adopting the combination coefficient to combine the predicted labels of all the features to obtain labels combining multiple features; classifying the natural image data on the basis of the labels combining the multiple features. The natural image classification method and device on the basis of the multi-modal matrix filling are easy to achieve, high classification accuracy can be obtained, meanwhile, the advantages of image classification on the basis of matrix filling are inherited, and the natural image classification method and device on the basis of the multi-modal matrix filling are suitable for the fields of network picture summarizing and classifying, image retrieval and the like.

Description

technical field [0001] The invention belongs to the technical field of image classification and multi-modal data analysis (multi-feature fusion), relates to a multi-label classification technology based on matrix filling, and specifically relates to an image classification method and device using multi-modal matrix filling. Background technique [0002] Different from images with single content and consistent form, such as faces and fingerprints, a natural image usually contains multiple objects, each of which presents different shapes. In natural image classification, it is often necessary to assign multiple class labels to an image. Such as figure 1 As shown, (a) "person" is riding a "bicycle", (b) "sky" and "ocean" often appear together, and (c) "dog" is a kind of "animal". Most of the traditional single-label classification (one sample has only one category label) algorithms cannot be directly used for multi-label classification. More feasible is the "one-to-many" str...

AI Technical Summary

Problems solved by technology

This approach will not only greatly reduce the calculation efficiency, but also lead to the problem of dimension explosion. At the same time, it lacks physical explanation and affects the classification accuracy.

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