Image scene labeling method based on conditional random field and secondary dictionary study

Abstract

The invention discloses an image scene labeling method based on a conditional random field and a secondary dictionary study, comprising steps of performing superpixel area over-segmentation on a training set image, obtaining a superpixel over-segmentation area of each image, extracting the characteristics of each superpixel over-segmentation area, combining with a standard labeled image to construct a superpixel label pool, using the superpixel label tool to train a support vector machine classifier to calculate superpixel unary potential energy, calculating paired item potential energy of adjacent superpixels, in virtue of global classification statistic of the over-segmentation superpixel area in a training set, constructing a classifier applicable to a class statistic histogram as a classification cost, using the histogram statistic based on the sum of the sparse coders of the sparse representation of the key point characteristic in each class superpixel area as the high order potential energy of a CRF model, using two distinguishing dictionaries of a class dictionary and a shared dictionary to optimize the sparse coder through the secondary sparse representation, and updating the dictionary, the CRF parameters and the classifier parameters. The image scene labeling method improves the labeling accuracy.

Description

technical field [0001] The invention relates to the technical field of image scene labeling, in particular to an image scene labeling method based on conditional random field and secondary dictionary learning. Background technique [0002] The basic problem of visual scene understanding is simply to extract semantic information in images. For a provided scene, it is necessary not only to use its visual color information, but also to infer the objects existing in the semantic scene based on prior knowledge, its spatial position relationship and dependency, as well as the application of the scene layout and various objects in the scene. complex activities. It is not difficult for humans to recognize these objects and associate them with information in the scene. The goal of scene understanding is to enable machines to effectively simulate human-specific innate functions, and extract relevant image semantic information to achieve effective image representation through prior s...

AI Technical Summary

Problems solved by technology

However, the literature [Tao Lingling, Porikli Fatih, Vidal René.Sparsedictionaries for semantic segmentation.Computer Vision–ECCV 2014.Springer,2014:549-564.] dictionaries are obtained through all categories of training, and it is impossible to distinguish which category the dictionary primitives belong to. Thus affecting the correctness of the target classification

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