Supergraph learning-based indoor scene classification method

Abstract

The invention, which relates to the indoor scene classification field, provides a supergraph learning-based indoor scene classification method. The method comprises the following steps that: a target is extracted from an image by using nearly a hundred of target detectors and a super descriptor formed by the formed target descriptor is used as a feature descriptor of the image; a supergraph of the image descriptor is constructed by using a K neighbor method and a Laplacian matrix is calculated, thereby constructing a semi-supervised learning frame; a linear regression model is constructed and is added into the semi-supervised learning frame; according to the constructed semi-supervised learning frame, marking is carried out on the part of image descriptor by combining the extracted image feature descriptor, so that the semi-supervised learning frame can predetermine a label of an unmarked image automatically and iteratively and thus the image classification is completed; and meanwhile, the linear regression model is initialized during the automatic iteration process; and according to the linear regression model, image classification is carried out on data that are added newly directly by combining the extracted image feature descriptor, so that there is no need to construct a supergraph again.

Description

technical field [0001] The invention relates to indoor scene classification, in particular to an indoor scene classification method based on hypergraph learning. Background technique [0002] At present, indoor scene classification generally uses low-level feature descriptors, which mainly include information such as color, texture, and shape. These low-level feature descriptors perform well for outdoor scene classification, but perform poorly for indoor scene classification due to the complex object types and overlaps in indoor scenes. With the development of related technologies, some improved image feature descriptors have been introduced to improve the classification effect of images, such as pyramid matching factors ([1] S.Lazebnik, C.Schmid, and J.Ponce, "Beyond bags of features:Spatial pyramid matching for recognizing natural scene categories,” in Proc.IEEE Int.Conf.Computer Vision and Pattern Recognition, 2006, vol.2, pp.2169–2178), global descriptors ([2]C.Siagian ...

AI Technical Summary

Problems solved by technology

However, these image classifications still use the commonly used fully supervised method for classification, which cannot comprehensively consider the relationship between the global attribute information and local data information of all data, so the performance in image classification is very general

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