Labelling image scene clustering method based on vision and labelling character related information

Abstract

The invention provides a labelling image scene clustering method based on vision and labelling character related information. The method comprises the following steps of: dividing a training image and a test image respectively by using a NCut (Normalized Cut) image dividing algorithm; constructing a vision nearest-neighbour graph G(C)(V, E) of all images {J1, ., Jl} PCtrain for learning, wherein in a training image set, each image has one group of initial normalized labelling character weight vectors; spreading the labelling character of each training image among the vision nearest neighbours, receiving the accepted images according to the degree of normalized EMD (Earth Mover's Distance) among the accepted images; for each training image, normalizing the accumulated labelling character weights; after the vision characteristics of the image are converted into a group of labelling characters with weights, carrying out the scene semantic clustering by using a PLSA (Probabilistic Latent Semantic Analysis) model; learning each scene semantic vision space by using a Gaussian mixture model; and carrying out the scene classification by using the vision characteristics. With the invention, the coupling precision between the vision characteristics of the image and the labelling character can be increased, and the method can be directly used for the automatic semantic labelling of the image.

Description

technical field [0001] The invention relates to an image processing method. Specifically, it is a method for automatic scene classification of images to be analyzed. Background technique [0002] In the field of image understanding such as automatic semantic annotation of images, to rely on visual features to classify non-annotated images, it is necessary to ensure that the proposed semantic scene categories are consistent in visual distribution. On the one hand, the semantic content that images can express is very rich, and an image placed in different environments may present different levels of information. On the other hand, due to the lack of description ability, the visual features of images have more obvious semantic ambiguity, and visually similar images cannot guarantee the consistency of semantic content. [0003] As a concise and efficient way to describe the high-level semantic content of images, image annotations provide a large number of reliable learning sam...

AI Technical Summary

Problems solved by technology

However, the inherent ambiguity of tag words (such as polysemy, multi-word synonym) also limits the effect of image clustering that only relies on image tag information.

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