Object classification method and system based on bag of visual word model

Abstract

The invention provides an object classification method and system based on a bag of visual word model. The method comprises the following steps: obtaining characteristic points of a sample picture and obtaining position information and description information of each characteristic point, wherein the sample picture comprises a first classification picture and a second classification picture; clustering the description information of each characteristic point so as to generate a visual dictionary taking the description information as visual terms; based on the description information of a target characteristic point in each characteristic point, finding one or more visual terms matching the description information of the target characteristic points; based on the position information of each characteristic point, calculating the weight of the description information of each characteristic point for the target characteristic points on the visual terms matching the target characteristic points; and through combination with all the target characteristic points, based on the position information of all the target characteristic points, generating a characteristic model, which is provided with space information and based on the weights of the visual terms, of the sample picture.

Description

technical field [0001] The present disclosure generally relates to the field of image processing, and more particularly relates to object classification techniques based on the bag-of-visual-words model. Background technique [0002] The bag of visual words model is one of the best methods in the field of object classification or object recognition. The model can express the characteristics of the target well and strive to obtain a higher recognition rate. [0003] The construction of the visual word bag model is based on the characteristics of feature points, so it is invariant to position, illumination, rotation and affine transformation. At the same time, the model is also robust to partial occlusions and offsets. However, since the traditional bag-of-visual-words model directly generates histogram features from all feature points in the target without considering the spatial information of the feature points in the target, it cannot obtain a better recognition rate. I...

AI Technical Summary

Problems solved by technology

However, since the traditional bag-of-words model directly generates histogram features from all the feature points in the target without considering the spatial information of the feature points in the target, it cannot obtain a better recognition rate.

In addition, the traditional object classification or object recognition method based on the visual bag of words model uses a lot of repeated calculations and consumes a lot of computing resources.

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