A migration sparse coding image classification method based on dictionary domain adaptation

Abstract

The invention discloses a migration sparse coding image classification method based on dictionary domain adaptation, belonging to the machine learning field. This method introduces dictionary alignment mechanism based on traditional sparse coding model and constructs feature transfer classification model to solve the problem of image cross-domain classification. When the samples in source domain and target domain obey different distributions, the traditional sparse-coded image classification algorithm can not learn from the samples in source domain to obtain the dictionary which can encode thesamples in target domain effectively, so the classification performance is degraded. At first, that dictionary alignment mechanism is introduce into a sparse coding model, then the constraint term ofthe dictionary are converted into an unconstrained optimization problem by L2 regularization, and the knowledge transfer performance of the model is improved by adopting an inter-domain dictionary approximation as a regularization term. The invention can effectively extract cross-domain image sparse feature representation and obtain higher classification accuracy.

Description

technical field [0001] The invention belongs to the field of machine learning, and in particular relates to a migration sparse coding image classification method based on dictionary domain adaptation. Background technique [0002] Image classification using machine learning methods is a popular research topic in the fields of machine vision and pattern recognition. Image classification technology refers to an image processing method that extracts features from image data with the help of a computer to form a description of the image content, and classifies the category of the image according to this description. This technology has broad application prospects, such as: content-based image retrieval in the Internet field, automatic classification of personal gallery, image recognition in the medical field, or face recognition and intelligent video analysis in the security field. At present, researchers have proposed many data-driven image classification algorithms, that is, ...

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Problems solved by technology

[0009] In view of the above existing technologies, a migration sparse coding image classification method based on dictionary domain adaptation is proposed, which solves the problem that when training and testing samples come from different distributions, different dictionaries are quantized for samples in different distribution fields, resulting in differences in coding features. which seriously affects the classification performance

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