The invention discloses an understanding method of a non-parametric RGB-D scene based on a probabilistic
graphical model. The method comprises the steps of carrying out global
feature matching between a marked image and an image marked in a training seat, and building a retrieval set of a similar image of an image to be marked;
cutting the image to be marked and the image in the similar
image retrieval set, so as to generate super-pixels, and carrying out characteristic extraction on the super-pixels extracted; calculating the proportions of all categories in the
training set, building a dictionary of rare categories, and taking the
training set and the retrieval set of the similar images as a
label source of the image to be marked; carrying out characteristics matching on each super-pixel of the image to be marked and all super-pixels in an image
label source; and building a probabilistic
graphical model, converting the maximum
posterior probability into a minimal energy function by using a Markov random field, and resolving the
semantic annotation of each super-pixel of the image to be marked obtained by solving the problem through a graph
cutting method. According to the method provided by the invention, the overall and local geometric information can be integrated, and the understanding performance of the RGB-D scene can be improved.