Image classification method based on multi-task multi-instance support vector machine

Abstract

The invention discloses an image classification method based on a multi-task multi-instance support vector machine. The method comprises the following steps: establishing T learning tasks for T groups of images; performing multi-instance processing on images of the T learning tasks; constructing one class package for each category of images in the T tasks; establishing an Euclidean distance formula from instances in the class packages to multi-instance packages; constructing instance distance vectors from the class packages to the multi-instance packages; establishing a weight Euclidean distance formula from the class packages to the multi-instance packages; performing constraining to enable distances from the multi-instance packages to their corresponding categories to be smaller than distances to other categories; establishing an optimization problem of the multi-task multi-instance support vector machine; converting the optimization problem into a problem of a conventional single-task single-instance support vector machine problem; and solving the optimization problem of the support vector machine. According to the method related to by the invention, the weight Euclidean distance formula is optimized, through performing instance processing on the images, a learning problem of the multi-task multi-instance support vector machine is established, an ideal weight is optimized, and thus performance of an image classifier is improved.

Description

technical field [0001] The invention relates to the technical field of image classification, in particular to an image classification method based on a multi-task and multi-instance support vector machine. Background technique [0002] With the advancement of information technology and the long-term development of social networks, there are already a large number of images on the Internet, and the number of images newly uploaded to the Internet every day is also increasing exponentially, and the scenes contained in images are becoming more and more abundant. The website has been developed for a long time, but the huge amount of pictures on the website has not been fully utilized, and a large number of new images are uploaded to the website every day, how to identify unmarked images and accurately classify them into corresponding categories To better serve website users is a problem that most Internet companies are studying. [0003] On the one hand, since the image may cont...

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Problems solved by technology

[0004] On the other hand, due to the openness of the Internet and the diversity of shooting equipment, photos of the same person may appear on different social networking sites, or taken by different equipment, or edited from different videos, and these pictures It is obviously unreasonable to mix together for recognition; moreover, in order to improve the performance of the image classifier, a large number of marked images are needed for the training of the classifier. If the training samples are insufficient, the performance of the classifier will decrease. Thus affecting the effect of image classification

Early image classification was done in the form of manual marking, but the artificial success of this method will be very high. It may be feasible under a small number of images, but it is not advisable at the current image generation speed of the Internet.

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