Image category detection method and system, electronic equipment and storage medium

Abstract

The invention discloses an image category detection method, and the method comprises the steps: obtaining a training image set, and constructing a training sample according to the image features of each training image in the training image set; training a classification model by using the training sample, wherein the classification model is a model constructed by a sparse Fisher support vector machine classification algorithm, and the sparse Fisher support vector machine is a Fisher support vector machine based on an L1 norm; and inputting a to-be-detected image into the trained classificationmodel to obtain the image type of the to-be-detected image. The image classification accuracy can be improved. The invention further discloses an image category detection system, electronic equipmentand a storage medium, which have the above beneficial effects.

Description

technical field [0001] The present application relates to the technical field of image processing, and in particular to an image type detection method and system, an electronic device and a storage medium. Background technique [0002] Image processing technology is a technology that uses computers to process image information. Image processing techniques may include image digitization, image enhancement and restoration, image data encoding, image segmentation, and image recognition. [0003] In the related art, there is a solution for classifying images based on image processing technology, for example, there is an operation of using Fisher regularized support vector machine to classify the authenticity of banknote images, so as to realize the identification of authentic banknotes. The Fisher regularization support vector machine can find a classification hyperplane and maximize the inter-class interval and minimize the intra-class divergence at the same time, but the mode...

AI Technical Summary

Problems solved by technology

The Fisher regularization support vector machine can find a classification hyperplane and maximize the inter-class interval and minimize the intra-class divergence at the same time, but the model constructed by the Fisher regularization support vector machine is not sparse, that is to say, the model will be affected by The influence of unimportant samples or noise samples in the training samples, which will lead to a decrease in the generalization performance of the classifier

Images