The invention discloses a method for identifying a place image on the basis of an improved probabilistic
topic model, belonging to the technical field of
image identification. The method provided by the invention can be used for well solving the problems that the
image identification is uncertain due to different angles, illumination, and height dynamic changes of figures and objects. The method comprises the following steps: an image acquiring step, an image preprocessing step, a
feature extraction step, a feature clustering step, a feature distribution step and a potential topic modeling step, wherein in the image acquiring step, the features of the image are extracted by adopting a SIFI (
scale invariant feature transform)
algorithm; in the feature clustering step, all the features are clustered so as to obtain a plurality of clustering centers; in the feature distribution step, the feature of each image is voted in the clustering center so as to obtain a
frequency vector corresponding to each clustering center; in the potential topic modeling step, the potential topic distribution of the image is learned by adopting the improved probabilistic
topic model; and a classifier is adopted to identify the images at unknown places. According to the invention, a quantization function is added in an LDA (
latent dirichlet allocation) model, and the potential topic of the image is learned through the improved probabilistic
topic model, so that the identification performance is effectively improved on the premise of guaranteeing instantaneity.