Method for identifying place image on the basis of improved probabilistic topic model

Abstract

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.

Description

technical field [0001] The invention relates to an image recognition method, in particular to a location image recognition method based on an improved probability topic model, which is used for indoor and outdoor location image recognition. technical background [0002] At present, as intelligent mobile robots enter people's daily life and play an important role in various fields such as military affairs, shopping malls, hospitals, and homes, people's demand for automatic positioning of intelligent mobile robot systems is becoming more and more urgent. Only when the mobile robot knows its own position and the working space accurately can it carry out autonomous movement safely and effectively and serve human beings. Therefore, self-localization and position estimation become one of the most important capabilities of autonomous mobile robots. The vision system can provide the most abundant perceptual information for mobile robots, and it also has the environment perception m...

AI Technical Summary

Problems solved by technology

[0004] However, the pictures actually obtained by the robot often have different shooting angles, different lighting, occlusions and even background confusion, and highly dynamic changes caused by changes in the positions of people and objects. These inherent variability lead to the process of location image recognition. not sure

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