Joint low-rank constraint cross-view discrimination subspace learning method and device

Abstract

The embodiment of the invention provides a joint low-rank constraint cross-view discrimination subspace learning method and device. The method comprises the following steps: defining an objective function of a double-low rank discrimination subspace learning formula; adopting a supervised regularization term as a strong constraint condition, and re-drawing up the target function; adding a joint heterogeneous regularization term to re-draw up the target function; dividing the image data set into a test set and a training set; utilizing the training set to solve the value of each variable when the objective function value is minimized; solving the objective function to obtain a feature subspace; and projecting the test set through the feature subspace to obtain all features of all types of images in the data set, and finally obtaining the recognition rate of the data set through a classifier. According to the method, heterogeneous regularization items are combined as constraints to construct discriminant items for feature learning, isomorphic and heterogeneous information of a sample can be projected to a subspace to be used in a discriminant learning model of an image recognition and classification task, and model adaptability and robustness are promoted.

Description

technical field [0001] Embodiments of the present invention relate to the field of image classification, and more specifically, embodiments of the present invention relate to a method and device for learning a cross-view discriminant subspace with joint low-rank constraints. Background technique [0002] Cross-view learning has attracted much attention in recent years, since our images are often acquired from various angles or from different sensor devices. In recent years, many cross-view discriminative subspace learning methods have been proposed, which have not only attracted much attention but also been successfully applied in real work. However, the discriminative models constructed by these methods only separate the samples of the same view and different categories, and the samples of different views of the same category are close to each other, ignoring the isomorphic and heterogeneous information hidden in different views. Contents of the invention [0003] In thi...

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Problems solved by technology

However, the discriminative models constructed by these methods only separate the samples of the same view and different categories, and the sample

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