Bag-of-visual-word model-based monitor video vehicle type classification method

Abstract

The invention discloses a bag-of-visual-word model-based monitor video vehicle type classification method. The method comprises the steps of collecting vehicle images; extracting vehicle eigenvectors from the vehicle images in a training image library; clustering the obtained vehicle eigenvectors to generate a visual dictionary; performing spatial pyramid decomposition on the extracted vehicle eigenvectors and the generated visual dictionary to obtain final image eigenvectors, and forming a training image model library; and extracting the final image eigenvectors from the to-be-classified images, performing classification by utilizing a KNN classifier, and outputting the types of the to-be-classified images. According to the method, the dimensions of the eigenvectors can be freely reduced by using a PCA-SIFT algorithm, so that the calculation amount is greatly reduced; and in addition, for an improved k-means algorithm, outliers and isolated points are removed by using a local factor anomaly algorithm, and then clustering is performed, so that the clustering precision of the k-means algorithm can be remarkably improved, the initial center selection is more targeted, and the calculation amount and the iterative frequency are reduced.

Description

technical field [0001] The invention relates to a video image classification method, in particular to a surveillance video car classification method based on a visual word bag model. Background technique [0002] With the development of electronic industry technology and multimedia technology, digital images and videos have been widely used in many fields, and information has shown explosive growth, forming a massive data environment. Compared with text information, image information is intuitive, vivid, and easy to understand, which brings a lot of convenience to people, but at the same time it also brings many problems. It becomes extremely difficult to select the required information, and there are often a large number of Useless or wrong information. The bag of words model comes from text classification. Text can be regarded as a collection of unordered words. By counting the words in the text, the classification of the text can be completed. Similar to image classific...

AI Technical Summary

Problems solved by technology

For these operations, the 128-dimensional array is too large, and the array contains a lot of redundant information, which will greatly increase the amount of calculation

And the k-means clustering algorithm is very sensitive to outliers and isolated points. If there are more outliers and isolated points in the feature vectors that need to be clustered, it will have a great impact on the clustering results. , thus affecting the accuracy of clustering

The selection of the initial clustering center of the K-means clustering algorithm is very important. If the initial clustering center is not well selected, the number of iterations will increase and the amount of calculation will be greater.

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