Image object recognition method based on SURF

Abstract

The invention provides an image object recognition method based on SURF (Speed Up Robust Feature), comprising the following steps: first, preprocessing images; second, extracting SURF corners and SURF descriptors of the images to describe the features of the images; third, processing the features through PCA data whitening and dimension reduction; establishing a bag-of-visual-words model through Kmeans clustering based on the features after processing, and using the bag-of-visual-words model to construct a visual vocabulary histogram of the images; and finally, carrying out training by a nonlinear support vector machine (SVM) classification method, and classifying the images to different categories. After classification model building of different images is completed in the training phase, the images tested in a concentrated way are detected in the testing phase, and therefore, different image objects can be recognized. The method has excellent performance in the aspects of recognition rate and speed, and can reflect the content of images more objectively and accurately. In addition, the classification result of an SVM classifier is optimized, and the error rate of judgment of the classifier and the limitation of the categories of training samples are reduced.

Description

technical field [0001] The invention relates to the field of image object recognition, in particular to an image object recognition method based on SURF (SpeedUpRobustFeature) features and a bag-of-words model. Background technique [0002] Image recognition and classification technology is an important application in the field of computer vision and pattern recognition. In machinery industry, logistics transportation, retail and other industries, accurate image object recognition technology can liberate people from heavy labor and reduce production costs. Improve work efficiency. In daily life, people can quickly and accurately grasp the characteristics of objects and recognize objects, but for computers, it is quite difficult to automatically recognize objects. The reason is that the objects in the image are affected by factors such as shooting angle, rotation change, illumination change, scale change, and shooting quality. [0003] The existing solution to these difficu...

AI Technical Summary

Problems solved by technology

In this patent document, the calculation of multi-scale SURF features requires a large amount of calculation, and the singular value decomposition is directly used to construct a dictionary without further processing of the features.

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