A Crowd Gathering Sensitive Image Detection Method

Abstract

The invention discloses a crowd gathering sensitive image detection method, which comprises the steps of: collecting image data in the gathering crowd, respectively obtaining image data sets of the original domain and the target domain; The transfer model of the common subspace and the sparse expression reconstruction matrix are learned at the same time, and the supervised discriminant regularization term is added to the transfer model; the common subspace of the classification task in the original domain and the target domain is obtained by using the image sample label information, and in the common subspace The optimization process is realized through transfer learning; the alternate optimization strategy and ADMM algorithm are used to alternately solve the optimization variables; sensitive images are identified. The present invention utilizes the establishment of a supervision and discrimination sparse transfer model to improve the accuracy of classification tasks in the case of small samples, thereby greatly improving the accuracy and discrimination of sensitive image recognition in complex environments such as crowd gatherings.

Description

technical field [0001] The invention belongs to the technical field of image detection, in particular to a crowd gathering sensitive image detection method. Background technique [0002] With the rapid growth of data set size and computing resources, the theory and application of artificial intelligence and its subfield machine learning have made great progress. Especially in the field of computer vision, the exponential explosion of data set size is particularly obvious. In real life, a large number of images are generated every day, but there are very few image data with label information, because manual labeling data consumes a huge amount of time and money. At the same time, in the classification of crowd-sensitive images, the tasks are changeable, and the scene and lighting changes greatly, making classification more difficult. [0003] In the framework of traditional machine learning, whether it is a classification or regression task, a mapping function or a classifi...

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Problems solved by technology

[0004] In order to solve the above problems, the present invention proposes a crowd-gathering-sensitive image detection method. Aiming at problems such as the scarcity of label samples in the target domain and the inconsistency of features, the establishment of a supervised discriminant sparse transfer model is used to improve the classification task in the case of small samples. accuracy, thus greatly improving the accuracy and discrimination of sensitive image recognition in complex environments such as crowd gathering

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