Image identification method and image identification device

Abstract

The application discloses an image identification method and an image identification device. First, image areas, matching a specified color, of sample images are standardized to get images in a first image set; then, an image classifier is trained by the first image set to get a trained image classifier; and finally, a to-be-identified image is input into the trained image classifier to get an identification result for the to-be-identified image. As the images in the first image set are obtained by processing the sample images, the percentage of the image area, matching a preset image category, in each image of the first image set is increased relatively, and information loss of the image areas, matching the preset image category, in the images during scaling of the images is reduced. Thus, through the method provided by the application, the number of images in the first image set is reduced effectively, and the training efficiency of the image classifier is improved while cost is reduced.

Description

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AI Technical Summary

Benefits of technology

The present technology allows an algorithm called Image Classifiers (CAs) to learn from samples with specific colors or gray levels based on their content. These CAs use these color values when recognizing different types of objects such as people's faces or cars. By doing so they improve object detection capabilities over time but at lower costs compared to traditional methods like pattern-matched filters.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving image recognition technologies without requiring excessively long time or expensive data collection efforts. Current techniques require manual annotation of images containing relevant attributes like background colors, faces, shapes, sizes, and other characteristics beforehand making these decisions based solely upon those predefined standards.

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