Conditional random field framework embedding registering information weak supervise image scene understanding method

Abstract

The invention discloses a conditional random field framework embedding registering information weak supervise image scene understanding method comprising the following steps: extracting training image characteristics; using a non-supervise algorithm to segment the training image so as to form a ultra pixel graph; considering structure relation information in the training image, between the training images and between registering ultra pixels, and using CRF to model a pixel mark training model; solving the model to obtain training image ultra pixel marks; combining the pixel mark training model with the extracted test image characteristics and the ultra pixel graph, the solved training image ultra pixel marks, the obtained structure relation information in the test image, between test images and between the test image and the registered training image, thus obtaining a modeling pixel mark testing model; solving the model to obtain ultra pixel marks in the test image. The method uses an image registering algorithm to dig the registering structure information between images, thus building the ultra pixel relations between the images; the registering information is introduced, thus effectively improving the multi-image model classification precision.

Description

technical field [0001] The invention relates to a weakly supervised image scene understanding method in which a conditional random field frame is embedded with registration information, and belongs to the technical field of computer vision. Background technique [0002] Scene image understanding is an important content in the field of computer vision research. It is the basis for higher-level semantic understanding of visual media content and plays a key role in applications such as image retrieval and automatic driving. In recent years, scene image understanding has been a research hotspot in academia and has attracted much attention from researchers. With conditional random fields as the basic framework, researchers have made gratifying progress in the design of scene image understanding algorithms. Among them, image understanding algorithms embedded with local smoothness, location, co-occurrence and other contextual prior information under CRF have achieved good results. ...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide a weakly supervised image scene understanding method in which the conditional random field frame is embedded with registration information, and solve the problem that the existing algorithms often only consider the similarity between superpixels in the feature space relationship, while ignoring the structural information between images, the problem of poor classification effect

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