Image Measurement Attribute Extraction Method under Large Error Condition

Abstract

In recent years, the rapid development of image processing based on deep learning has had a wide impact on industrial production and social life. In the field of information security, image content description as a kind of attribute has been used in authentication, access control and other technologies. In addition to describing what is in an image, fine-grained image content properties should also be able to express the relative size and position relationships between objects. Due to the strong fuzziness of the relative scale between objects in the image, it is not suitable for precise definition. This paper proposes a method to extract measurement attributes under large error to describe the relative size of two parts in an image. Based on image classification and object detection, the error range is estimated by statistical method, and a fuzzy set is constructed as the length unit. Then the contradictory critical points of measurement are used to determine the comparable scale range and similar regions. Finally, the measurement attribute of image content is given. Experiments show that the size description obtained by this method accords with people’s subjective feelings, and can effectively extract the fine-grained attributes of the image.