Image classification method based on visual dictionary

Abstract

The invention discloses an image classification method based on a visual dictionary and relates to the technical field of digital image processing. The image classification method comprises the following steps of: 1, extracting a union partial characteristic of a training image data set; 2, performing vector vectorization on the union partial characteristic by using a clustering algorithm based on a moving mean value and a regional hash method so as to select the number of clustering centers and form the visual dictionary; 3, generating a characteristic expression of images according to the visual dictionary so as to build an image classifier; and 4, classifying the images in the training image data set according to the image classifier. By the image classification method, the visual dictionary having the discrimination can be obtained, so the classification method is adaptive to the sample space distribution of the image data set, high in resistance of affine transformation and lighting variation, robustness to partial abnormity, noise interference and complicated backgrounds, universality and practical value, and can be applied to classification of various images.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image classification method based on a visual dictionary. Background technique [0002] Image classification is to determine the category of the target in the image, so as to classify the image. At present, image classification technology has become an important research direction in artificial intelligence and pattern recognition, and has been applied in military target recognition, remote sensing and medical image recognition, OCR, biometric recognition, bill recognition, intelligent transportation and other fields. [0003] Technically speaking, content-based image classification mainly uses low-level local features and high-level semantic features of images to establish feature-based vector representations, thereby transforming them into supervised learning problems in the field of artificial intelligence. In actual use, the difficulty of image classifica...

AI Technical Summary

Problems solved by technology

The commonly used vectorization method is k-means (a hard clustering algorithm), but this algorithm generally assumes that the data set is a mixed normal distribution, and it is easy to fall into local extremum where the sample points are densely distributed during the iterative process. Moreover, the number of clusters must be determined in advance. Different initial points may result in different results. A sample point far away from the center point may cause a large deviation from the center point, and the calculation complexity is high. In actual use, the optimal Discriminative Visual Dictionary

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